Appendix N — Bibliography
Sources are listed in chapter order. Follow Cited in to return to the source’s context. Recommended reading is listed separately.
Entries retain the citation label when publication details are incomplete. Inclusion in this bibliography does not establish that a source supports every claim made about it.
Search results
- 2024 ACG/ASGE Quality Indicators for ColonoscopyCited in: Surgical Subspecialties
- 2024 position statementCited in: Allergy, Immunology, and Medical Genetics
- 2024 practice guidelinesCited in: Allergy, Immunology, and Medical Genetics
- 2024 updateCited in: Ophthalmology
- 2025 education strategyCited in: Surgery, Anesthesiology, and Perioperative Care
- 3DermRecommended in: AI Tools Every Physician Should Know
- 510(k) ClearancesCited in: Physician AI Liability and Regulatory ComplianceRecommended in: Evaluating AI Clinical Decision Support Systems
- 510(k) K242130Cited in: Internal Medicine and Hospital Medicine
- AAFP survey report, 2025Cited in: Primary Care, Family Medicine, and Preventive Medicine
- aafp.org/artificial-intelligenceCited in: Primary Care, Family Medicine, and Preventive Medicine
- AAMC, 2025Cited in: AI in Medical Education
- AAO Diabetic Retinopathy PPPCited in: Ophthalmology (passage 1); Ophthalmology (passage 2)
- AAO-HNS Clinical Practice Guideline on ARHL (2024)Cited in: Surgical Subspecialties
- AAOS Position Statement 1193Cited in: Orthopedic Surgery and Physical Medicine (passage 1); Orthopedic Surgery and Physical Medicine (passage 2); Surgical SubspecialtiesRecommended in: Orthopedic Surgery and Physical Medicine (passage 3)
- AB 406Cited in: Psychiatry and Behavioral Health
- Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability ChallengesQaiser Abbas; Woonyoung Jeong; Seung Won Lee. 2025. Healthcarejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Abbott, February 2026Cited in: Internal Medicine and Hospital Medicine
- Abdul Jabbar et al., Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration, 2024Cited in: Neurology and Neurological Surgery
- Educational Strategies for Clinical Supervision of Artificial Intelligence UseRaja-Elie E. Abdulnour; Brian Gin; Christy K. Boscardin. 2025. New England Journal of Medicinejournal articleCited in: AI in Medical Education (passage 1); AI in Medical Education (passage 2)
- Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomised controlled trialWilliam T Abraham; Philip B Adamson; Robert C Bourge; et al. 2011. The Lancetjournal articleCited in: Internal Medicine and Hospital Medicine; Cardiology and Cardiothoracic Surgery; Emerging AI Technologies in Healthcare
- AbridgeRecommended in: AI Tools Every Physician Should Know
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: Executive Summary; History of AI in Medicine (passage 1); History of AI in Medicine (passage 2); Diagnostic Imaging, Radiology, and Nuclear Medicine; Primary Care, Family Medicine, and Preventive Medicine (passage 1); Primary Care, Family Medicine, and Preventive Medicine (passage 2); Medical Ethics, Bias, and Health Equity (passage 1); Medical Ethics, Bias, and Health Equity (passage 2); Medical Ethics, Bias, and Health Equity (passage 3); Integration into Clinical Workflow; Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2); AI Tools Every Physician Should Know (passage 1); AI Tools Every Physician Should Know (passage 2); Healthcare Policy and AI Governance; Quick Reference: All Chapter Summaries (TL;DRs); Clinical Case Study Library
- Generative versus discriminative diagnostic performance of large multimodal models in intracranial and spinal neuroradiologyAlaeddin Acar; Bahadir Kaya; Diaa Yahya; et al. 2026. npj Digital Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- Accenture Technology VisionRecommended in: Industry Reports and Benchmarks
- ACGMECited in: Medical AI Career Guide
- ACGME, 2025Cited in: AI in Medical Education
- Association of Use of the Neonatal Early-Onset Sepsis Calculator With Reduction in Antibiotic Therapy and SafetyNiek B. Achten; Claus Klingenberg; William E. Benitz; et al. 2019. JAMA Pediatricsjournal articleCited in: Pediatrics and Neonatology
- ACOG and SMFM, 2021Cited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2)
- ACOG Committee Opinion 664Cited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2)
- ACOG Committee Opinion 819Cited in: Obstetrics and Gynecology
- ACOG, 2026Cited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2); Obstetrics and Gynecology (passage 3)
- acog.orgRecommended in: Obstetrics and Gynecology
- ACR Appropriateness Criteria for suspected pulmonary embolismCited in: Medical Ethics, Bias, and Health Equity
- ACR approval and effective-date noticeCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- ACR ARCH-AI programCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- ACR Assess-AI registryCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- ACR-SIIM Practice Parameter for Imaging AICited in: Executive Summary; Diagnostic Imaging, Radiology, and Nuclear Medicine; Physician AI Liability and Regulatory Compliance; Healthcare Policy and AI Governance; The Physician-AI Partnership: Future PerspectivesRecommended in: Evaluating AI Clinical Decision Support Systems
- ADA 2025/2026 Standards of Care Section 7Cited in: Internal Medicine and Hospital Medicine
- Ada HealthRecommended in: AI Tools Every Physician Should Know
- Adam et al., 2026, preprintCited in: Emerging AI Technologies in Healthcare
- HITECH Act Drove Large Gains In Hospital Electronic Health Record AdoptionJulia Adler-Milstein; Ashish K. Jha. 2017. Health Affairsjournal articleCited in: Healthcare Policy and AI Governance
- African AVE Collaborative, Lancet Glob Health, 2026Cited in: Obstetrics and Gynecology
- afrimedqa.comRecommended in: AI and Global Health Equity
- A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-BeingMajid Afshar; Mary Ryan Baumann; Felice Resnik; et al. 2025. NEJM AIjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine; AI Tools Every Physician Should Know; AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2); AI-Assisted Clinical Documentation (passage 3)
- AGA Clinical Practice Update on AI in Polyp DiagnosisCited in: Surgical Subspecialties
- AGA Living Clinical Practice Guideline on CADe-Assisted ColonoscopyCited in: Surgical Subspecialties
- Racial–Ethnic Disparities in Diabetes Technology use Among Young Adults with Type 1 DiabetesShivani Agarwal; Clyde Schechter; Jeffrey Gonzalez; et al. 2021. Diabetes Technology & Therapeuticsjournal articleCited in: Pediatrics and Neonatology
- Agarwal et al., 2023Cited in: Clinical AI Safety and Risk Management
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction in Klebsiella pneumoniae: A Systematic Review of Clinical Microbiology ApplicationsRaghav Aggarwal; Nisarg Shah; Jolie Jin En Wong; et al. 2026. Infection and Drug Resistancejournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trialAmbrose Agweyu; Paul Mwaniki; Vaishnavi Menon; et al. 2026. Nature Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine; Evaluating AI Clinical Decision Support Systems; AI Tools Every Physician Should Know; Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2); Clinical Trials and AI; Clinical Case Study Library
- Safety of a large language model-based clinical decision support system in African primary healthcareAmbrose Agweyu; Paul Mwaniki; Wilkister Musau; et al. 2026. Nature Healthjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine; Integration into Clinical Workflow; AI and Global Health Equity
- AHA News, 2017Cited in: Healthcare Policy and AI Governance
- aha.org/topics/artificial-intelligence-aiRecommended in: Industry Reports and Benchmarks
- AHCA/NCAL, 2026Cited in: Emerging AI Technologies in Healthcare
- Machine Learning Augmented Interpretation of Chest X-rays: A Systematic ReviewHassan K. Ahmad; Michael R. Milne; Quinlan D. Buchlak; et al. 2023. Diagnosticsjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- A rapid literature review of the impact of penicillin allergy on antibiotic resistanceShadia Ahmed; Jonathan A T Sandoe. 2025. JAC-Antimicrobial Resistancejournal articleCited in: Allergy, Immunology, and Medical Genetics
- Five-year cost-effectiveness of AI for adult diabetic eye exams—a health system perspectiveMahnoor Ahmed; Michael D. Abramoff; Harold P. Lehmann; et al. 2026. npj Digital Medicinejournal articleCited in: Ophthalmology (passage 1); Ophthalmology (passage 2); Ophthalmology (passage 3); Ophthalmology (passage 4)
- AI Resource HubCited in: AI Tools Every Physician Should KnowRecommended in: Internal Medicine and Hospital Medicine (passage 1); Internal Medicine and Hospital Medicine (passage 2); Further Reading and Resources
- AidocRecommended in: AI Tools Every Physician Should Know
- AIM-HER2 Breast CancerCited in: Pathology and Laboratory Medicine
- Ajmal et al., 2026, preprintCited in: Evaluating AI Clinical Decision Support Systems
- Ajmani et al., 2025, preprintCited in: Psychiatry and Behavioral Health
- Artificial Intelligence Identifies Factors Associated with Blood Loss and Surgical Experience in CholecystectomyJosiah G. Aklilu; Min Woo Sun; Shelly Goel; et al. 2024. NEJM AIjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care (passage 1); Surgery, Anesthesiology, and Perioperative Care (passage 2)
- Al-Ghufaily et al., 2026Cited in: Orthopedic Surgery and Physical Medicine
- Medical large language models are vulnerable to data-poisoning attacksDaniel Alexander Alber; Zihao Yang; Anton Alyakin; et al. 2025. Nature Medicinejournal articleCited in: Medical Misinformation and AI
- Alberdi et al., 2004Cited in: History of AI in Medicine
- Enhancing clinical documentation with ambient artificial intelligence: a quality improvement survey assessing clinician perspectives on work burden, burnout, and job satisfactionMichael Albrecht; Denton Shanks; Tina Shah; et al. 2025. JAMIA Openjournal articleCited in: AI Tools Every Physician Should Know
- ESMO basic requirements for AI-based biomarkers in oncology (EBAI)M. Aldea; M. Salto-Tellez; A. Marra; et al. 2026. Annals of Oncologyjournal articleCited in: Hematology-Oncology and Precision Medicine
- Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendationsJoseph E Alderman; Joanne Palmer; Elinor Laws; et al. 2025. The Lancet Digital Healthjournal articleCited in: Evaluating AI Clinical Decision Support Systems; Medical Ethics, Bias, and Health Equity
- Aleph SurgicalCited in: Surgery, Anesthesiology, and Perioperative Care
- Ali et al., 2024Cited in: Surgical Subspecialties
- Artificial Hallucinations in ChatGPT: Implications in Scientific WritingHussam Alkaissi; Samy I McFarlane. 2023. Cureusjournal articleCited in: Emerging AI Technologies in Healthcare
- Machine Learning-Enabled Medical Devices Authorized by the US Food and Drug Administration in 2024: Regulatory Characteristics, Predicate Lineage, and Transparency ReportingBassel Almarie; Luis Fernando Gonzalez-Gonzalez; Lucas Antônio dos Santos Barbosa; et al. 2025. Biomedicinesjournal articleCited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- Menstrual Cycle Variations in Wearable-Detected Finger Temperature and Heart Rate, But Not in Sleep Metrics, in Young and Midlife IndividualsElisabet Alzueta; Marie Gombert-Labedens; Harold Javitz; et al. 2024. Journal of Biological Rhythmsjournal articleCited in: Obstetrics and Gynecology
- AMA AI Principles, 2018Cited in: Healthcare Policy and AI GovernanceRecommended in: Clinical AI Policy Templates
- AMA Augmented Intelligence in MedicineCited in: Psychiatry and Behavioral HealthRecommended in: Evaluating AI Clinical Decision Support Systems; Further Reading and Resources
- AMA eight-step health-system AI governance toolkitRecommended in: Evaluating AI Clinical Decision Support Systems; AI Vendor Evaluation Framework
- AMA Physician Innovation NetworkRecommended in: Course Syllabus Template
- AMA Physician Survey on Augmented IntelligenceRecommended in: Industry Reports and Benchmarks
- AMA Policy on Augmented Intelligence in Health CareRecommended in: Course Syllabus Template
- AMA PolicyFinder, 2025Cited in: AI in Medical Education
- AMA, 2024Cited in: Healthcare Policy and AI Governance
- AMA, 2025Cited in: Healthcare Policy and AI Governance
- AMA, February 2025Cited in: Large Language Models in Clinical Practice
- AMA, March 2026Cited in: AI Fundamentals for Clinicians
- ama-assn.org/practice-management/digital-healthRecommended in: Industry Reports and Benchmarks
- Real-World Single-Reading Screening Mammography Performance When Using an FDA-Approved Artificial Intelligence ToolEmily B. Ambinder; Colin Paulbeck; Babita Panigrahi; et al. 2026. American Journal of Roentgenologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- American Academy of Neurology position-statement directoryRecommended in: Neurology and Neurological Surgery
- American Board of Preventive MedicineCited in: Medical AI Career Guide
- American Medical Informatics Association GuidelinesRecommended in: Clinical AI Policy Templates
- America’s AI Action PlanCited in: Healthcare Policy and AI Governance
- AMIA Annual SymposiumRecommended in: Further Reading and Resources
- Machine Learning Prediction of Mortality and Hospitalization in Heart Failure With Preserved Ejection FractionSuveen Angraal; Bobak J. Mortazavi; Aakriti Gupta; et al. 2020. JACC: Heart Failurejournal articleCited in: Cardiology and Cardiothoracic Surgery
- Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural NetworksMauro Annarumma; Samuel J. Withey; Robert J. Bakewell; et al. 2019. Radiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Limits of Artificial Intelligence Models for Skin Cancer Diagnosis in Realistic SettingsJulien Anriot; Siyuan Yan; Clio Coste; et al. 2026. JAMA Dermatologyjournal articleCited in: Dermatology
- Anthropic Research, 2025Recommended in: AI Tools Every Physician Should Know
- Anthropic Research, 2025Recommended in: AI Tools Every Physician Should Know
- Anthropic, 2025Cited in: Medical Ethics, Bias, and Health Equity
- Anthropic, 2025Cited in: Psychiatry and Behavioral Health
- Anthropic, 2026Cited in: AI and Global Health Equity
- A new version of Antibiogo is now available!Antibiogo. 2025. Official Antibiogo product updateCited in: Infectious Diseases and Antimicrobial Stewardship
- AntibiogoAntibiogo. 2026. Official Antibiogo product siteCited in: Infectious Diseases and Antimicrobial Stewardship
- The TIMI Risk Score for Unstable Angina/Non–ST Elevation MIElliott M. Antman; Marc Cohen; Peter J. L. M. Bernink; et al. 2000. JAMAjournal articleCited in: Cardiology and Cardiothoracic Surgery
- APA Position Statement on the Role of Augmented Intelligence in Clinical Practice and Research (PDF)Cited in: Psychiatry and Behavioral Health
- APA Practice OrganizationRecommended in: AI Vendor Evaluation Framework
- The Use of Generative Artificial Intelligence (AI) in Academic Research: A Review of the Consensus AppOlukayode E Apata; Oi-Man Kwok; Yuan-Hsuan Lee. 2025. Cureusjournal articleCited in: AI Tools Every Physician Should Know
- apply for BAARecommended in: AI Tools Every Physician Should Know
- Prediction of preterm birth in nulliparous women using logistic regression and machine learningReza Arabi Belaghi; Joseph Beyene; Sarah D. McDonald. 2021. PLOS ONEjournal articleCited in: Obstetrics and Gynecology
- Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integrationMohammad R. Arbabshirani; Brandon K. Fornwalt; Gino J. Mongelluzzo; et al. 2018. npj Digital Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine; Emergency Medicine; Neurology and Neurological Surgery
- The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemiaDaniel A. Arber; Attilio Orazi; Robert Hasserjian; et al. 2016. Bloodjournal articleCited in: Pediatrics and Neonatology
- AREDS2-HOME Study Research Group, 2014Cited in: Ophthalmology
- ARISE MAST methodologyCited in: Evaluating AI Clinical Decision Support Systems
- Arizona H.B. 2177Cited in: Healthcare Policy and AI Governance
- Arizona HB 2175Cited in: Physician AI Liability and Regulatory Compliance
- Artificial intelligence in clinical trials—state of the evidence, gaps, and next stepsAntonis A. Armoundas; Constantine Tarabanis; Joseph Loscalzo. 2026. eClinicalMedicinejournal articleCited in: Clinical Trials and AI
- Artificial Intelligence and Machine Learning for Primary Care curriculumCited in: Primary Care, Family Medicine, and Preventive Medicine
- Artificial Intelligence and Machine Learning: Transforming Surgical Practice and EducationRecommended in: Surgery, Anesthesiology, and Perioperative Care
- Artificial Intelligence in Surgery resource pageRecommended in: Surgery, Anesthesiology, and Perioperative Care
- Artificial Intelligence ResourcesRecommended in: Neurology and Neurological Surgery
- Artificial Intelligence/Machine Learning-Based Software as a Medical Device Action PlanRecommended in: Course Syllabus Template
- OpenEvidence clinical question-answering platform: systematic review of early evaluationsYaara Artsi; Vera Sorin; Benjamin S. Glicksberg; et al. 2026. npj Digital Medicinejournal articleCited in: AI Tools Every Physician Should Know; Large Language Models in Clinical Practice; AI in Medical Education
- ASCO, 2025Cited in: Hematology-Oncology and Precision Medicine
- A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisationElham Asgari; Nina Montaña-Brown; Magda Dubois; et al. 2025. npj Digital Medicinejournal articleCited in: Large Language Models in Clinical Practice
- ASH, 2026Cited in: Hematology-Oncology and Precision Medicine
- ASH, Subcommittee on Artificial IntelligenceCited in: Hematology-Oncology and Precision Medicine
- A Competency-Based Privileging Model for Robotic PlatformsBinita S. Ashar; Jesse C. Selber. 2026. JAMA Surgeryjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Assran et al., 2025Cited in: Cardiology and Cardiothoracic Surgery
- Athenahealth, March 2026Cited in: AI Fundamentals for Clinicians
- An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome predictionZachi I Attia; Peter A Noseworthy; Francisco Lopez-Jimenez; et al. 2019. The Lancetjournal articleCited in: Physician AI Liability and Regulatory Compliance
- Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogramZachi I. Attia; Suraj Kapa; Francisco Lopez-Jimenez; et al. 2019. Nature Medicinejournal articleCited in: Cardiology and Cardiothoracic Surgery (passage 1); Cardiology and Cardiothoracic Surgery (passage 2); Cardiology and Cardiothoracic Surgery (passage 3)
- AUA 2025Cited in: Surgical Subspecialties
- AUA 2025 reportCited in: Surgical Subspecialties
- AUA Advocacy, June 2024Cited in: Surgical Subspecialties
- AUA has no standalone AI clinical practice guidelineCited in: Surgical Subspecialties
- AUA Policy and Position StatementsCited in: Surgical Subspecialties
- AUA Privacy PolicyCited in: Surgical Subspecialties
- AUA/SUO Early Detection of Prostate Cancer GuidelineCited in: Surgical Subspecialties
- A clinical decision support tool for accurate hip fracture prediction: A nationwide cohort studyKristian F. Axelsson; Henrik Litsne; Konstantinos Konstantinou; et al. 2026. PLOS Medicinejournal articleCited in: Orthopedic Surgery and Physical Medicine
- Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media ForumJohn W. Ayers; Adam Poliak; Mark Dredze; et al. 2023. JAMA Internal Medicinejournal articleCited in: Emerging AI Technologies in Healthcare
- American Academy of Otolaryngology–Head and Neck Surgery (AAO‐HNS) Report on Artificial IntelligenceNoel F. Ayoub; Anaïs Rameau; Michael J. Brenner; et al. 2025. Otolaryngology–Head and Neck Surgeryjournal articleCited in: Surgical Subspecialties
- Principles to guide clinical AI readiness and move from benchmarks to real-world evaluationTej D. Azad; Harlan M. Krumholz; Suchi Saria. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems; Clinical AI Safety and Risk Management
- Machine Learning–Guided Detection of Malignancy of Lung Nodules With Molecular Imaging–Guided SurgeryFeredun Azari; Gregory T. Kennedy; Andrew Hanna; et al. 2026. JAMA Network Openjournal articleCited in: Hematology-Oncology and Precision Medicine
- Comparative Efficacy of Different AI Systems for Polyp Detection by Size During Colonoscopy: Systematic Review and Network Meta-AnalysisLing Ba; Yaxin Qi; Xinrui Lv; et al. 2026. Journal of Medical Internet Researchjournal articleCited in: Surgical Subspecialties
- Patterns of AI Use in Clinical Work by Hospitalists: Survey StudyPrabhava Bagla; Jasmah Hanna; Bhargav Marthambadi; et al. 2026. Journal of Medical Internet Researchjournal articleCited in: Internal Medicine and Hospital Medicine
- AI-assisted cervical cytology precancerous screening for high-risk population in resource-limited regions using a compact microscopeJiaxin Bai; Ning Li; Hua Ye; et al. 2025. Nature Communicationsjournal articleCited in: Obstetrics and Gynecology
- Medicaid Work Requirements—The Physician as ArbiterHenry Bair. 2026. JAMA Internal Medicinejournal articleCited in: Healthcare Policy and AI Governance
- Bakker et al., Hypothesis 2023Cited in: AI Tools Every Physician Should Know
- Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational studyHeling Bao; Hui Bi; Xiaosong Zhang; et al. 2020. Gynecologic Oncologyjournal articleCited in: Obstetrics and Gynecology; Pathology and Laboratory Medicine
- PhysSFI-Net: physics-informed geometric learning of skeletal and facial interactions for orthognathic surgical outcome predictionJiahao Bao; Huazhen Liu; Yu Zhuang; et al. 2026. npj Digital Medicinejournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Implementing an Antibiotic Stewardship Program: Guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of AmericaTamar F. Barlam; Sara E. Cosgrove; Lilian M. Abbo; et al. 2016. Clinical Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- DXplainG. Octo Barnett. 1987. JAMAjournal articleCited in: AI Tools Every Physician Should Know
- Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alertsJason M Baron; Richard Huang; Dustin McEvoy; et al. 2021. JAMIA Openjournal articleCited in: Internal Medicine and Hospital Medicine
- Barron et al., 2018Cited in: AI and Global Health Equity
- Symptom monitoring with electronic patient-reported outcomes during cancer treatment: final results of the PRO-TECT cluster-randomized trialEthan Basch; Deborah Schrag; Jennifer Jansen; et al. 2025. Nature Medicinejournal articleCited in: Hematology-Oncology and Precision Medicine
- Generative AI without guardrails can harm learning: Evidence from high school mathematicsHamsa Bastani. 2025. Proceedings of the National Academy of Sciencesjournal articleCited in: AI in Medical Education
- Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods StudySanjay Basu; Bhairavi Muralidharan; Parth Sheth; et al. 2025. JMIR AIjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Mechanistic interpretability of reinforcement learning in Medicaid care coordinationSanjay Basu; Sadiq Patel; Parth Sheth; et al. 2026. BMJ Health & Care Informaticsjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a RealityDavid W. Bates; Gilad J. Kuperman; Samuel Wang; et al. 2003. Journal of the American Medical Informatics Associationjournal articleCited in: Primary Care, Family Medicine, and Preventive MedicineRecommended in: Internal Medicine and Hospital Medicine
- SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand UltrasoundChristian F. Baumgartner; Konstantinos Kamnitsas; Jacqueline Matthew; et al. 2017. IEEE Transactions on Medical Imagingjournal articleCited in: Obstetrics and Gynecology
- Challenges to the Reproducibility of Machine Learning Models in Health CareAndrew L. Beam; Arjun K. Manrai; Marzyeh Ghassemi. 2020. JAMAjournal articleCited in: AI-Assisted Clinical Documentation; Clinical Research with AI
- Beam and Kohane, 2018Cited in: AI Fundamentals for Clinicians; Surgery, Anesthesiology, and Perioperative Care; Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2); Physician AI Liability and Regulatory Compliance (passage 3)
- Reliability of LLMs as medical assistants for the general public: a randomized preregistered studyAndrew M. Bean; Rebecca Elizabeth Payne; Guy Parsons; et al. 2026. Nature Medicinejournal articleCited in: Executive Summary; Evaluating AI Clinical Decision Support Systems; Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2); AI Failures in Medicine: Lessons Learned
- Beaubier et al., 2019Cited in: AI Tools Every Physician Should Know
- Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?Brett K. Beaulieu-Jones; William Yuan; Gabriel A. Brat; et al. 2021. npj Digital Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance
- Becker’s Hospital Review, January 2026Cited in: Healthcare Policy and AI Governance; Clinical Case Study Library
- Fidelity of Medical Reasoning in Large Language ModelsSuhana Bedi; Yixing Jiang; Philip Chung; et al. 2025. JAMA Network Openjournal articleCited in: AI Fundamentals for Clinicians; Evaluating AI Clinical Decision Support Systems (passage 1); Evaluating AI Clinical Decision Support Systems (passage 2); Evaluating AI Clinical Decision Support Systems (passage 3); Evaluating AI Clinical Decision Support Systems (passage 4); Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2); Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2); Emerging AI Technologies in Healthcare; The Physician-AI Partnership: Future Perspectives; AI Failures in Medicine: Lessons Learned
- Testing and Evaluation of Health Care Applications of Large Language ModelsSuhana Bedi; Yutong Liu; Lucy Orr-Ewing; et al. 2025. JAMAjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- MedHELM: Holistic evaluation of large language models in medicineSuhana Bedi. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems; Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic RetinopathyEmma Beede; Elizabeth Baylor; Fred Hersch; et al. 2020. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systemsconference paperCited in: Emerging AI Technologies in Healthcare; AI Failures in Medicine: Lessons Learned; AI Vendor Evaluation Framework
- External Validation of an Artificial Intelligence Algorithm Using Biparametric MRI and Its Simulated Integration with Conventional PI-RADS for Prostate Cancer DetectionMason J. Belue; Vaneeza Mukhtar; Roopa Ram; et al. 2025. Academic Radiologyjournal articleCited in: Surgical Subspecialties
- Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical NeurophysiologySándor Beniczky; Samuel Wiebe; Jesper Jeppesen; et al. 2021. Epilepsiajournal articleCited in: Neurology and Neurological Surgery (passage 1); Neurology and Neurological Surgery (passage 2)
- A Licensure Framework for Autonomous Clinical AIAlon Bergman. 2026. JAMAjournal articleCited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- Operationalization of Artificial Intelligence Applications in the Intensive Care UnitWillemijn E. M. Berkhout; Julia J. van Wijngaarden; Jessica D. Workum; et al. 2025. JAMA Network Openjournal articleCited in: Critical Care and Pulmonary Medicine
- Randomized Study of the Impact of AI on Perceived Legal Liability for RadiologistsMichael H. Bernstein; Brian Sheppard; Michael A. Bruno; et al. 2025. NEJM AIjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine; Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- The radiologist–AI workflow and the risk of medical malpractice claimsMichael H. Bernstein; Brian Sheppard; Michael A. Bruno; et al. 2026. Nature Healthjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine; Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with DiabetesMalavika Bhaskaranand; Chaithanya Ramachandra; Sandeep Bhat; et al. 2019. Diabetes Technology & Therapeuticsjournal articleCited in: AI Tools Every Physician Should Know
- High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical ContentMehul Bhattacharyya; Valerie M Miller; Debjani Bhattacharyya; et al. 2023. Cureusjournal articleCited in: Emerging AI Technologies in Healthcare
- BI-RADSRecommended in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- MySurgeryRisk: Development and Validation of a Machine-learning Risk Algorithm for Major Complications and Death After SurgeryAzra Bihorac; Tezcan Ozrazgat-Baslanti; Ashkan Ebadi; et al. 2019. Annals of Surgeryjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care (passage 1); Surgery, Anesthesiology, and Perioperative Care (passage 2)
- Does timing matter—meta-analysis and systematic review of digital cognitive behavioral therapy in the perinatal periodCathelijne Bijen; Stephanie Homan; Marta A. Marciniak. 2026. npj Digital Medicinejournal articleCited in: Obstetrics and Gynecology
- Reinforcement learning based automated anesthesia system for gastrointestinal endoscopy with a multicenter randomized trialHai-Long Bing; Yan Wang; Shi-Ying Li; et al. 2026. npj Digital Medicinejournal articleCited in: Surgical Subspecialties
- BJR|Open, 2024Cited in: AI Tools Every Physician Should Know
- Building safer clinical agents: the case for residency-level benchmarks in medical artificial intelligenceKameron C Black. 2026. The Lancet Digital Healthjournal articleCited in: Evaluating AI Clinical Decision Support Systems
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- Wu et al., NEJM AI, 2024Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- Biomarkers in neonatal encephalopathy: new approaches and ongoing questionsCourtney J. Wusthoff. 2022. Pediatric Researchjournal articleCited in: Pediatrics and Neonatology
- www.aapmr.orgRecommended in: Orthopedic Surgery and Physical Medicine
- www.apta.orgRecommended in: Orthopedic Surgery and Physical Medicine
- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisalLaure Wynants; Ben Van Calster; Gary S Collins; et al. 2020. BMJjournal articleCited in: Executive Summary
- Wyoming Legislature, 2025Cited in: Healthcare Policy and AI Governance
- Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay peopleXuhai ‘Orson’ Xu; Haoyu Hu; Haoran Zhang; et al. 2026. Nature Medicinejournal articleCited in: Dermatology
- “Small” Large Language Models in the Hospital: Evaluation Study on Real-World Data in a Resource-Constrained SettingHe A Xu; Romain Pythoud; Christian W Thorball; et al. 2026. Journal of Medical Internet Researchjournal articleCited in: Large Language Models in Clinical Practice
- Xu et al., 2025Cited in: Pathology and Laboratory Medicine
- Deep learning-assisted versus manual reading in routine cervical cytopathology: a multicentre randomised crossover trialPeng Xue; Hongping Tang; Haiyan Weng; et al. 2026. npj Digital Medicinejournal articleCited in: Obstetrics and Gynecology
- Y CombinatorCited in: Emerging AI Technologies in Healthcare
- Yahya et al., 2025Cited in: Allergy, Immunology, and Medical Genetics
- A Deep Learning Mammography-based Model for Improved Breast Cancer Risk PredictionAdam Yala; Constance Lehman; Tal Schuster; et al. 2019. Radiologyjournal articleCited in: Obstetrics and Gynecology
- Initial lessons from real-world implementation of an AI-agent eye clinic in ChinaTao Yan; Di Zhang; Luxiao Chen; et al. 2026. Nature Medicinejournal articleCited in: Ophthalmology; Integration into Clinical Workflow
- Tracking cancer lesions on surgical samples of gastric cancer by artificial intelligent algorithmsRuixin Yang; Chao Yan; Sheng Lu; et al. 2021. Journal of Cancerjournal articleCited in: Pathology and Laboratory Medicine
- Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective studyZijian Yang; Yibo Zhang; Lili Zhuo; et al. 2024. European Journal of Cancerjournal articleCited in: Pathology and Laboratory Medicine
- The limits of fair medical imaging AI in real-world generalizationYuzhe Yang; Haoran Zhang; Judy W. Gichoya; et al. 2024. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- An online randomized trial of electronic nudges for influenza vaccination willingness among older Chinese adultsFan Yang; Ban Hu; Ning Huang; et al. 2026. npj Digital Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine (passage 1); Primary Care, Family Medicine, and Preventive Medicine (passage 2)
- Evaluating the clinical utility of large language models for hepatocellular carcinoma treatment recommendations: A nationwide retrospective registry studyKeungmo Yang; Jaejun Lee; Jeong Won Jang; et al. 2026. PLOS Medicinejournal articleCited in: Hematology-Oncology and Precision Medicine
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligenceZichen Ye; Yue Chen; Xuefeng Huang; et al. 2026. npj Digital Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studiesNayeon Yi; Dain Baik; Gumhee Baek. 2025. Journal of Nursing Scholarshipjournal articleCited in: Emergency Medicine
- Performance of Emergency Department Screening Criteria for an Early ECG to Identify ST‐Segment Elevation Myocardial InfarctionMaame Yaa A. B. Yiadom; Christopher W. Baugh; Conor M. McWade; et al. 2017. Journal of the American Heart Associationjournal articleCited in: Cardiology and Cardiothoracic Surgery
- Real-Time AI-Assisted Insulin Titration System for Glucose Control in Patients With Type 2 DiabetesZhen Ying; Yujuan Fan; Congling Chen; et al. 2025. JAMA Network Openjournal articleCited in: Internal Medicine and Hospital Medicine
- Phenomapping-derived tool to individualize the effect of sacubitril-valsartan in heart failure with preserved ejection fractionMinjae Yoon; Wonse Kim; Jin Joo Park; et al. 2026. npj Digital Medicinejournal articleCited in: Cardiology and Cardiothoracic Surgery
- You et al., 2020Cited in: The Physician-AI Partnership: Future Perspectives
- Infectious Diseases Society of America Position Statement on Telehealth and Telemedicine as Applied to the Practice of Infectious DiseasesJeremy D Young; Rima Abdel-Massih; Thomas Herchline; et al. 2019. Clinical Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Antimicrobial Selection by a ComputerVictor L. Yu. 1979. JAMAjournal articleCited in: History of AI in Medicine (passage 1); History of AI in Medicine (passage 2); Quick Reference: All Chapter Summaries (TL;DRs)
- Heterogeneity and predictors of the effects of AI assistance on radiologistsFeiyang Yu; Alex Moehring; Oishi Banerjee; et al. 2024. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine; Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2); Clinical AI Safety and Risk Management (passage 3)
- Multicenter study on the versatility and adoption of AI-driven automated radiotherapy planning across cancer typesLei Yu; Qianxi Ni; Binbing Wang; et al. 2025. Nature Communicationsjournal articleCited in: Hematology-Oncology and Precision Medicine
- Technological functionality and system architecture of mobile health interventions for diabetes management: a systematic review and meta-analysis of randomized controlled trialsXinran Yu; Yifeng Wang; Zhengyang Liu; et al. 2025. Frontiers in Public Healthjournal articleCited in: Internal Medicine and Hospital Medicine
- Prompt Engineering Paradigms for Medical Applications: Scoping ReviewJamil Zaghir; Marco Naguib; Mina Bjelogrlic; et al. 2024. Journal of Medical Internet Researchjournal articleCited in: Large Language Models in Clinical Practice (passage 1)Recommended in: Large Language Models in Clinical Practice (passage 2)
- Mapping AI startup investment and innovation in healthcare using a five-tier AI systems complexity frameworkAhmed Zahlan; Pek Hooi Soh; Bart Clarysse. 2026. npj Digital Medicinejournal articleCited in: Industry Reports and Benchmarks
- Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell DiseaseRima S. Zahr; Akram Mohammed; Surabhi Naik; et al. 2024. American Journal of Nephrologyjournal articleCited in: Hematology-Oncology and Precision Medicine
- A randomised controlled trial of an automated oxygen delivery algorithm for preterm neonates receiving supplemental oxygen without mechanical ventilationJames Zapata; John Jairo Gómez; Robinson Araque Campo; et al. 2014. Acta Paediatricajournal articleCited in: Pediatrics and Neonatology
- Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional studyJohn R. Zech; Marcus A. Badgeley; Manway Liu; et al. 2018. PLOS Medicinejournal articleCited in: AI Fundamentals for Clinicians; The Clinical Data Challenge (passage 1); The Clinical Data Challenge (passage 2); The Clinical Data Challenge (passage 3); Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2)
- Comparison of Initial Artificial Intelligence (AI) and Final Physician Recommendations in AI-Assisted Virtual Urgent Care VisitsDan Zeltzer; Zehavi Kugler; Lior Hayat; et al. 2025. Annals of Internal Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Artificial Intelligence–Driven Video-Based Surgical Skill Assessment in Hepatobiliary Laparoscopic SurgeryXiaojun Zeng; Yuchong Li; Junfeng Wang; et al. 2026. JAMA Surgeryjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- PRIMARY-AI: outcomes-based standards to safeguard primary care in the AI eraDian Zeng; Lorainne Tudor Car; Kamlesh Khunti; et al. 2026. Nature Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Zeng et al., JAMA, 2025Cited in: AI and Global Health Equity
- Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis DiagnosisShao Zhang; Jianing Yu; Xuhai Xu; et al. 2024. Proceedings of the CHI Conference on Human Factors in Computing Systemsconference paperCited in: Emerging AI Technologies in Healthcare
- A Multimodal large language model-based triage tool for osteoporotic vertebral compression fractures using posture and movement videosMeiwei Zhang; Xiaoqing Jin; Shicai Xu; et al. 2026. npj Digital Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine (passage 1); Primary Care, Family Medicine, and Preventive Medicine (passage 2)
- Effect of a clinical decision support system on stroke care quality and outcomes in patients with acute ischaemic stroke (GOLDEN BRIDGE II): cluster randomised clinical trialXinmiao Zhang; Lingling Ding; Jing Jing; et al. 2026. BMJjournal articleCited in: Neurology and Neurological Surgery; Evaluating AI Clinical Decision Support Systems (passage 1); Evaluating AI Clinical Decision Support Systems (passage 2)
- Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trialXiaoming Zhang; Chunli Li; Xu Han; et al. 2026. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- On-premise medical AI agents for reliable clinical decision-makingLi Zhang; Georg Wölflein; Dyke Ferber; et al. 2026. Nature Medicinejournal articleCited in: AI Fundamentals for Clinicians; Evaluating AI Clinical Decision Support Systems; Clinical AI Safety and Risk Management
- The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery and DevelopmentHarrison G. Zhang; Peter Eckmann; Jiacheng Miao; et al. 2026preprintCited in: Hematology-Oncology and Precision Medicine
- Zhang et al., 2023Cited in: Emerging AI Technologies in Healthcare
- Zhang et al., 2024Cited in: Evaluating AI Clinical Decision Support Systems
- Zhang et al., 2026Cited in: Ophthalmology (passage 1); Ophthalmology (passage 2)
- DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural networkZhidong Zhao; Yanjun Deng; Yang Zhang; et al. 2019. BMC Medical Informatics and Decision Makingjournal articleCited in: Obstetrics and Gynecology
- Gene expression signatures from whole blood predict amyotrophic lateral sclerosis case status and survivalYue Zhao; Masha G. Savelieff; Xiayan Li; et al. 2025. Nature Communicationsjournal articleCited in: Neurology and Neurological Surgery
- Uncertainty-inspired open-set model for identifying infantile fundus abnormalitiesXinyu Zhao; Zhenquan Wu; Xuemei Zhu; et al. 2026. npj Digital Medicinejournal articleCited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
- Concordance Study Between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in ChinaNa Zhou; Chuan-Tao Zhang; Hong-Ying Lv; et al. 2019. The Oncologistjournal articleCited in: Clinical Case Study Library; AI Failures in Medicine: Lessons Learned; AI Vendor Evaluation Framework
- MedVersa: A Generalist Foundation Model for Diverse Medical Imaging TasksHong-Yu Zhou; Julián Nicolás Acosta; Subathra Adithan; et al. 2026. NEJM AIjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- No Effect of Computer-Aided Diagnosis on Colonoscopic Adenoma Detection in a Large Pragmatic Multicenter Randomized StudyKatharina Zimmermann-Fraedrich; Susanne Sehner; Thomas Rösch; et al. 2026. American Journal of Gastroenterologyjournal articleCited in: Surgical Subspecialties
- Trajectories of the heart rate characteristics index, a physiomarker of sepsis in premature infants, predict Neonatal ICU mortalityAmanda M Zimmet; Brynne A Sullivan; J Randall Moorman; et al. 2020. JRSM Cardiovascular Diseasejournal articleCited in: Pediatrics and Neonatology
- Racial, Ethnic, and Language Disparities in Early Childhood Developmental/Behavioral EvaluationsKatharine E. Zuckerman; Kimber M. Mattox; Brianna K. Sinche; et al. 2014. Clinical Pediatricsjournal articleCited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
- Ötleş et al., 2026Cited in: Healthcare Policy and AI Governance
Executive Summary
References
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: Executive Summary
- ACR-SIIM Practice Parameter for Imaging AICited in: Executive Summary
- Reliability of LLMs as medical assistants for the general public: a randomized preregistered studyAndrew M. Bean; Rebecca Elizabeth Payne; Guy Parsons; et al. 2026. Nature Medicinejournal articleCited in: Executive Summary
- Performance of a large language model on the reasoning tasks of a physicianPeter G. Brodeur; Thomas A. Buckley; Zahir Kanjee; et al. 2026. Sciencejournal articleCited in: Executive Summary
- FDA AI-Enabled Medical DevicesCited in: Executive Summary
- FDA CDS Guidance, January 2026Cited in: Executive Summary
- FDA DEN180001Cited in: Executive Summary
- Computer aided detection and diagnosis of polyps in adult patients undergoing colonoscopy: a living clinical practice guidelineFarid Foroutan; Per Olav Vandvik; Lise M Helsingen; et al. 2025. BMJjournal articleCited in: Executive Summary
- GPT-4 assistance for improvement of physician performance on patient care tasks: a randomized controlled trialEthan Goh; Robert J. Gallo; Eric Strong; et al. 2025. Nature Medicinejournal articleCited in: Executive Summary
- A meta-analysis of Watson for Oncology in clinical applicationZhou Jie; Zeng Zhiying; Li Li. 2021. Scientific Reportsjournal articleCited in: Executive Summary
- Understanding Liability Risk from Using Health Care Artificial Intelligence ToolsMichelle M. Mello; Neel Guha. 2024. New England Journal of Medicinejournal articleCited in: Executive Summary
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Executive Summary
- ChatGPT Health performance in a structured test of triage recommendationsAshwin Ramaswamy; Alvira Tyagi; Hannah Hugo; et al. 2026. Nature Medicinejournal articleCited in: Executive Summary
- Ross & Swetlitz, 2018Cited in: Executive Summary
- scheduled to take effect October 1, 2026Cited in: Executive Summary
- Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction ModelAndrew Wong. 2026. JAMA Network Openjournal articleCited in: Executive Summary
- Wong et al., 2021Cited in: Executive Summary
- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisalLaure Wynants; Ben Van Calster; Gary S Collins; et al. 2020. BMJjournal articleCited in: Executive Summary
Part I: Foundations
History of AI in Medicine
References
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: History of AI in Medicine (passage 1); History of AI in Medicine (passage 2)
- Alberdi et al., 2004Cited in: History of AI in Medicine
- Brown et al., 2020Cited in: History of AI in Medicine
- Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational studyKrzysztof Budzyń; Marcin Romańczyk; Diana Kitala; et al. 2025. The Lancet Gastroenterology & Hepatologyjournal articleCited in: History of AI in Medicine
- Turing-like indistinguishability tests for the validation of a computer simulation of paranoid processesKenneth Mark Colby; Franklin Dennis Hilf; Sylvia Weber; et al. 1972. Artificial Intelligencejournal articleCited in: History of AI in Medicine
- Colby et al., 1966Cited in: History of AI in Medicine
- Dahmani & Bohbot, 2020Cited in: History of AI in Medicine
- DENDRAL (1965)Cited in: History of AI in Medicine
- DXplain (1984-present)Cited in: History of AI in Medicine
- ELIZACited in: History of AI in Medicine
- Dermatologist-level classification of skin cancer with deep neural networksAndre Esteva; Brett Kuprel; Roberto A. Novoa; et al. 2017. Naturejournal articleCited in: History of AI in Medicine
- FDA AI-Enabled Medical DevicesCited in: History of AI in Medicine
- FDA decision letterCited in: History of AI in Medicine
- FDA DEN180001Cited in: History of AI in Medicine (passage 1); History of AI in Medicine (passage 2)
- Adversarial attacks on medical machine learningSamuel G. Finlayson; John D. Bowers; Joichi Ito; et al. 2019. Sciencejournal articleCited in: History of AI in Medicine
- Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studiesKaroline Freeman; Jacqueline Dinnes; Naomi Chuchu; et al. 2020. BMJjournal articleCited in: History of AI in Medicine
- Detecting influenza epidemics using search engine query dataJeremy Ginsberg; Matthew H. Mohebbi; Rajan S. Patel; et al. 2009. Naturejournal articleCited in: History of AI in Medicine
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsVarun Gulshan; Lily Peng; Marc Coram; et al. 2016. JAMAjournal articleCited in: History of AI in Medicine
- INTERNIST-I/CADUCEUS (1974-1985)Cited in: History of AI in Medicine
- A meta-analysis of Watson for Oncology in clinical applicationZhou Jie; Zeng Zhiying; Li Li. 2021. Scientific Reportsjournal articleCited in: History of AI in Medicine (passage 1); History of AI in Medicine (passage 2)
- Khullar, 2025Cited in: History of AI in Medicine
- Kohn et al., 2000Cited in: History of AI in Medicine
- Krizhevsky et al., 2012Cited in: History of AI in Medicine
- Lambert et al., 2024, preprintCited in: History of AI in Medicine
- The Parable of Google Flu: Traps in Big Data AnalysisDavid Lazer; Ryan Kennedy; Gary King; et al. 2014. Sciencejournal articleCited in: History of AI in Medicine
- Lee et al., 2024Cited in: History of AI in Medicine
- Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided DetectionConstance D. Lehman; Robert D. Wellman; Diana S. M. Buist; et al. 2015. JAMA Internal Medicinejournal articleCited in: History of AI in Medicine (passage 1); History of AI in Medicine (passage 2); History of AI in Medicine (passage 3)
- McCarthy et al., 1955Cited in: History of AI in Medicine
- Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancerKunal Nagpal; Davis Foote; Yun Liu; et al. 2019. npj Digital Medicinejournal articleCited in: History of AI in Medicine
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: History of AI in Medicine
- Long-term impact of computer-aided adenoma detection: a prospective observational studyTaishi Okumura; Shin-ei Kudo; Yutaro Ide; et al. 2026. Endoscopyjournal articleCited in: History of AI in Medicine
- ONCOCIN (1981)Cited in: History of AI in Medicine
- PARRYCited in: History of AI in Medicine
- Learning and deskilling effects of artificial intelligence in colonoscopy among endoscopists with different levels of experience: a pragmatic, prospective trialTom Andre Pedersen; Yuichi Mori; Edoardo Botteri; et al. 2026. Endoscopyjournal articleCited in: History of AI in Medicine
- Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsiesPatricia Raciti; Jillian Sue; Rodrigo Ceballos; et al. 2020. Modern Pathologyjournal articleCited in: History of AI in Medicine
- Rajpurkar et al., 2017Cited in: History of AI in Medicine
- Ross & Swetlitz, 2018Cited in: History of AI in Medicine (passage 1); History of AI in Medicine (passage 2)
- Mapping the Bibliometrics Landscape of AI in Medicine: Methodological StudyJin Shi; David Bendig; Horst Christian Vollmar; et al. 2023. Journal of Medical Internet Researchjournal articleCited in: History of AI in Medicine
- Computer-based consultations in clinical therapeutics: Explanation and rule acquisition capabilities of the MYCIN systemEdward H. Shortliffe; Randall Davis; Stanton G. Axline; et al. 1975. Computers and Biomedical Researchjournal articleCited in: History of AI in Medicine (passage 1); History of AI in Medicine (passage 2)
- Large language models encode clinical knowledgeKaran Singhal; Shekoofeh Azizi; Tao Tu; et al. 2023. Naturejournal articleCited in: History of AI in Medicine
- Watson for Oncology and breast cancer treatment recommendations: agreement with an expert multidisciplinary tumor boardS.P. Somashekhar; M.-J. Sepúlveda; S. Puglielli; et al. 2018. Annals of Oncologyjournal articleCited in: History of AI in Medicine
- Strickland, 2019Cited in: History of AI in Medicine (passage 1); History of AI in Medicine (passage 2)
- Wong et al., 2021Cited in: History of AI in Medicine
- Antimicrobial Selection by a ComputerVictor L. Yu. 1979. JAMAjournal articleCited in: History of AI in Medicine (passage 1); History of AI in Medicine (passage 2)
AI Fundamentals for Clinicians
References
- AMA, March 2026Cited in: AI Fundamentals for Clinicians
- Athenahealth, March 2026Cited in: AI Fundamentals for Clinicians
- Beam and Kohane, 2018Cited in: AI Fundamentals for Clinicians
- Fidelity of Medical Reasoning in Large Language ModelsSuhana Bedi; Yixing Jiang; Philip Chung; et al. 2025. JAMA Network Openjournal articleCited in: AI Fundamentals for Clinicians
- AI for radiographic COVID-19 detection selects shortcuts over signalAlex J. DeGrave; Joseph D. Janizek; Su-In Lee. 2021. Nature Machine Intelligencejournal articleCited in: AI Fundamentals for Clinicians (passage 1); AI Fundamentals for Clinicians (passage 2)
- FDA AI-Enabled Medical DevicesCited in: AI Fundamentals for Clinicians
- Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncologyDyke Ferber; Omar S. M. El Nahhas; Georg Wölflein; et al. 2025. Nature Cancerjournal articleCited in: AI Fundamentals for Clinicians
- Adversarial attacks on medical machine learningSamuel G. Finlayson; John D. Bowers; Joichi Ito; et al. 2019. Sciencejournal articleCited in: AI Fundamentals for Clinicians (passage 1); AI Fundamentals for Clinicians (passage 2)
- Automation bias: a systematic review of frequency, effect mediators, and mitigatorsKate Goddard; Abdul Roudsari; Jeremy C Wyatt. 2012. Journal of the American Medical Informatics Associationjournal articleCited in: AI Fundamentals for Clinicians
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsVarun Gulshan; Lily Peng; Marc Coram; et al. 2016. JAMAjournal articleCited in: AI Fundamentals for Clinicians (passage 1); AI Fundamentals for Clinicians (passage 2)
- P-1295. Use of Large Language Models Does Not Improve Model Performance for Antimicrobial Resistance Prediction in Community- and Hospital-Onset Gram Negative SepsisAlison M Hixon; Hanyang Liu; Michael J Durkin; et al. 2026. Open Forum Infectious Diseasesjournal articleCited in: AI Fundamentals for Clinicians
- Jiang et al., 2025Cited in: AI Fundamentals for Clinicians
- Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancerKunal Nagpal; Davis Foote; Yun Liu; et al. 2019. npj Digital Medicinejournal articleCited in: AI Fundamentals for Clinicians
- Adoption of Artificial Intelligence in the Health Care SectorThuy D. Nguyen; Christopher M. Whaley; Kosali Simon; et al. 2025. JAMA Health Forumjournal articleCited in: AI Fundamentals for Clinicians
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: AI Fundamentals for Clinicians (passage 1); AI Fundamentals for Clinicians (passage 2)
- OpenClawCited in: AI Fundamentals for Clinicians
- Rajpurkar et al., 2017Cited in: AI Fundamentals for Clinicians
- Stop explaining black box machine learning models for high stakes decisions and use interpretable models insteadCynthia Rudin. 2019. Nature Machine Intelligencejournal articleCited in: AI Fundamentals for Clinicians
- Artificial intelligence agents in cancer research and oncologyDaniel Truhn; Shekoofeh Azizi; James Zou; et al. 2026. Nature Reviews Cancerjournal articleCited in: AI Fundamentals for Clinicians
- Utah Office of AI Policy, 2026Cited in: AI Fundamentals for Clinicians
- WHO, 2025Cited in: AI Fundamentals for Clinicians
- Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional studyJohn R. Zech; Marcus A. Badgeley; Manway Liu; et al. 2018. PLOS Medicinejournal articleCited in: AI Fundamentals for Clinicians
- On-premise medical AI agents for reliable clinical decision-makingLi Zhang; Georg Wölflein; Dyke Ferber; et al. 2026. Nature Medicinejournal articleCited in: AI Fundamentals for Clinicians
The Clinical Data Challenge
References
- Disparities in dermatology AI performance on a diverse, curated clinical image setRoxana Daneshjou; Kailas Vodrahalli; Roberto A. Novoa; et al. 2022. Science Advancesjournal articleCited in: The Clinical Data Challenge (passage 1); The Clinical Data Challenge (passage 2); The Clinical Data Challenge (passage 3)
- The Clinician and Dataset Shift in Artificial IntelligenceSamuel G. Finlayson; Adarsh Subbaswamy; Karandeep Singh; et al. 2021. New England Journal of Medicinejournal articleCited in: The Clinical Data Challenge (passage 1); The Clinical Data Challenge (passage 2)
- Tokenising the patient journey: from records to representationsFaisal Mahmood; Eric J Topol. 2026. The Lancetjournal articleCited in: The Clinical Data Challenge
- Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devicesViraj Mehta; Abhinav Komanduri; Rishabh Singh Bhadouriya; et al. 2025. npj Digital Medicinejournal articleCited in: The Clinical Data Challenge
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: The Clinical Data Challenge
- Simulating mismatch between calibration and target population in AI for mammography the retrospective VAIB studyHaiko Schurz; Klara Solander; Davida Åström; et al. 2025. npj Digital Medicinejournal articleCited in: The Clinical Data Challenge
- Detecting and Remediating Harmful Data Shifts for the Responsible Deployment of Clinical AI ModelsVallijah Subasri; Amrit Krishnan; Ali Kore; et al. 2025. JAMA Network Openjournal articleCited in: The Clinical Data Challenge
- Wong et al., 2021Cited in: The Clinical Data Challenge
- Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional studyJohn R. Zech; Marcus A. Badgeley; Manway Liu; et al. 2018. PLOS Medicinejournal articleCited in: The Clinical Data Challenge (passage 1); The Clinical Data Challenge (passage 2); The Clinical Data Challenge (passage 3)
Industry Reports and Benchmarks
References
- KLAS ResearchCited in: Industry Reports and Benchmarks (passage 1)Recommended in: Industry Reports and Benchmarks (passage 2)
- Nature Medicine editorial, 2026Cited in: Industry Reports and Benchmarks
- Mapping AI startup investment and innovation in healthcare using a five-tier AI systems complexity frameworkAhmed Zahlan; Pek Hooi Soh; Bart Clarysse. 2026. npj Digital Medicinejournal articleCited in: Industry Reports and Benchmarks
Recommended reading
- Accenture Technology VisionRecommended in: Industry Reports and Benchmarks
- aha.org/topics/artificial-intelligence-aiRecommended in: Industry Reports and Benchmarks
- AMA Physician Survey on Augmented IntelligenceRecommended in: Industry Reports and Benchmarks
- ama-assn.org/practice-management/digital-healthRecommended in: Industry Reports and Benchmarks
- CB Insights State of Digital HealthRecommended in: Industry Reports and Benchmarks
- cbinsights.com/researchRecommended in: Industry Reports and Benchmarks
- chai.orgRecommended in: Industry Reports and Benchmarks
- Coalition for Health AI Applied Model CardRecommended in: Industry Reports and Benchmarks
- Deloitte Global Health Care OutlookRecommended in: Industry Reports and Benchmarks (passage 1); Industry Reports and Benchmarks (passage 2)
- ECRI AI ResourcesRecommended in: Industry Reports and Benchmarks (passage 1); Industry Reports and Benchmarks (passage 2)
- Federal Health IT Strategic Plan 2024-2030Recommended in: Industry Reports and Benchmarks
- himss.org/news-centerRecommended in: Industry Reports and Benchmarks
- HIMSS/Medscape AI Adoption in Health Systems ReportRecommended in: Industry Reports and Benchmarks
- McKinsey Healthcare AI ResearchRecommended in: Industry Reports and Benchmarks
- McKinsey healthcare insightsRecommended in: Industry Reports and Benchmarks
- ONC Reports to CongressRecommended in: Industry Reports and Benchmarks (passage 1); Industry Reports and Benchmarks (passage 2)
- RAND Health, Health Care, and Aging researchRecommended in: Industry Reports and Benchmarks (passage 1); Industry Reports and Benchmarks (passage 2)
- Rock HealthRecommended in: Industry Reports and Benchmarks (passage 1); Industry Reports and Benchmarks (passage 2)
- Silicon Valley Bank Healthcare Investments and ExitsRecommended in: Industry Reports and Benchmarks
- Stanford AI IndexRecommended in: Industry Reports and Benchmarks (passage 1); Industry Reports and Benchmarks (passage 2)
Part II: AI Across Clinical Specialties
Diagnostic Imaging, Radiology, and Nuclear Medicine
References
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Generative versus discriminative diagnostic performance of large multimodal models in intracranial and spinal neuroradiologyAlaeddin Acar; Bahadir Kaya; Diaa Yahya; et al. 2026. npj Digital Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- ACR approval and effective-date noticeCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- ACR ARCH-AI programCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- ACR Assess-AI registryCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- ACR-SIIM Practice Parameter for Imaging AICited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Machine Learning Augmented Interpretation of Chest X-rays: A Systematic ReviewHassan K. Ahmad; Michael R. Milne; Quinlan D. Buchlak; et al. 2023. Diagnosticsjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Real-World Single-Reading Screening Mammography Performance When Using an FDA-Approved Artificial Intelligence ToolEmily B. Ambinder; Colin Paulbeck; Babita Panigrahi; et al. 2026. American Journal of Roentgenologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural NetworksMauro Annarumma; Samuel J. Withey; Robert J. Bakewell; et al. 2019. Radiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integrationMohammad R. Arbabshirani; Brandon K. Fornwalt; Gino J. Mongelluzzo; et al. 2018. npj Digital Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Randomized Study of the Impact of AI on Perceived Legal Liability for RadiologistsMichael H. Bernstein; Brian Sheppard; Michael A. Bruno; et al. 2025. NEJM AIjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- The radiologist–AI workflow and the risk of medical malpractice claimsMichael H. Bernstein; Brian Sheppard; Michael A. Bruno; et al. 2026. Nature Healthjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- AI performance varies considerably across mammography devices: a multi-site and multi-vendor retrospective studyMarcel Blum; Rudolf Morant; Alena Eichenberger; et al. 2026. Insights into Imagingjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNAAdrian P. Brady; Bibb Allen; Jaron Chong; et al. 2024. Radiology: Artificial Intelligencejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational studyKrzysztof Budzyń; Marcin Romańczyk; Diana Kitala; et al. 2025. The Lancet Gastroenterology & Hepatologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- AI for radiographic COVID-19 detection selects shortcuts over signalAlex J. DeGrave; Joseph D. Janizek; Su-In Lee. 2021. Nature Machine Intelligencejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- Interpretable deep learning for three-stage focal liver lesion diagnosis on non-contrast MRI: a multicenter, prospective studyShunjie Dong; Zhehan Shen; Ji Xia; et al. 2026. npj Digital Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- Nationwide real-world implementation of AI for cancer detection in population-based mammography screeningNora Eisemann; Stefan Bunk; Trasias Mukama; et al. 2025. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trialEsperanza Elías-Cabot; Sara Romero-Martín; José Luis Raya-Povedano; et al. 2026. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 3)
- ESR, 2025Cited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- FDA AI-Enabled Medical DevicesCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 3); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 4)
- FDA CDS FAQsCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- FDA CDS Guidance, January 2026Cited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- FDA K251934 recordCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- FDA K251934 summaryCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- FDA K252970 recordCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- FDA K260898 recordCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- FDA PCCP guidanceCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Viz.ai Implementation of Stroke Augmented Intelligence and Communications Platform to Improve Indicators and Outcomes for a Comprehensive Stroke Center and NetworkM.E. Figurelle; D.M. Meyer; E.S. Perrinez; et al. 2023. American Journal of Neuroradiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- The scientific evidence of commercial AI products for MRI acceleration: a systematic reviewStefan J. Fransen; Christian Roest; Frank F. J. Simonis; et al. 2025. European Radiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trialJessie Gommers; Veronica Hernström; Viktoria Josefsson; et al. 2026. The Lancetjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 3)
- CLEAR: an auditable foundation model for radiology grounded in clinical conceptsTianyu Han; Riga Wu; Yu Tian; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Hickman et al., 2023Cited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest RadiographsJames M. Hillis; Bernardo C. Bizzo; Sarah Mercaldo; et al. 2022. JAMA Network Openjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Efficiency and Quality of Generative AI–Assisted Radiograph ReportingJonathan Huang; Matthew T. Wittbrodt; Caitlin N. Teague; et al. 2025. JAMA Network Openjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative SummaryBin Jiang; Nancy Pham; Eric K. van Staalduinen; et al. 2025. Radiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studiesChristopher J. Kelly; Marc Wilson; Lucy M. Warren; et al. 2026. Nature Cancerjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Teaching AI for Radiology Applications: A Multisociety-Recommended Syllabus from the AAPM, ACR, RSNA, and SIIMFelipe Kitamura; Timothy Kline; Daniel Warren; et al. 2025. Radiology: Artificial Intelligencejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- The Effect of AI on the Radiologist Workforce: A Task-Based AnalysisCurtis P. Langlotz. 2025preprintCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosisAgostina J. Larrazabal; Nicolás Nieto; Victoria Peterson; et al. 2020. Proceedings of the National Academy of Sciencesjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Performance across different versions of an artificial intelligence model for screen-reading of mammogramsMarthe Larsen; Christoph I. Lee; Marie B. Bergan; et al. 2026. European Radiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping reviewRachel Lawrence; Emma Dodsworth; Efthalia Massou; et al. 2025. eClinicalMedicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Early Recalls and Clinical Validation Gaps in Artificial Intelligence–Enabled Medical DevicesBranden Lee; Patrick Kramer; Sara Sandri; et al. 2025. JAMA Health Forumjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided DetectionConstance D. Lehman; Robert D. Wellman; Diana S. M. Buist; et al. 2015. JAMA Internal Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysisChristian Leibig; Moritz Brehmer; Stefan Bunk; et al. 2022. The Lancet Digital Healthjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approachWilliam Lotter; Abdul Rahman Diab; Bryan Haslam; et al. 2021. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy studyKristina Lång; Viktoria Josefsson; Anna-Maria Larsson; et al. 2023. The Lancet Oncologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 3)
- Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment TimesJuan Carlos Martinez-Gutierrez; Youngran Kim; Sergio Salazar-Marioni; et al. 2023. JAMA Neurologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 3)
- International evaluation of an AI system for breast cancer screeningScott Mayer McKinney; Marcin Sieniek; Varun Godbole; et al. 2020. Naturejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Understanding Liability Risk from Using Health Care Artificial Intelligence ToolsMichelle M. Mello; Neel Guha. 2024. New England Journal of Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Economic Value of AI in Radiology: A Systematic ReviewIsabel Molwitz; Inka Ristow; Jennifer Erley; et al. 2026. Radiology: Artificial Intelligencejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- multisociety pediatric radiology statementCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- AI Improves Nodule Detection on Chest Radiographs in a Health Screening Population: A Randomized Controlled TrialJu Gang Nam; Eui Jin Hwang; Jayoun Kim; et al. 2023. Radiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- NCT04949776Cited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Impact of Deep Learning-Based Computer-Aided Detection and Electronic Notification System for Pneumothorax on Time to Treatment: Clinical ImplementationSi Nae Oh; Hyungkook Yang; Chun Kyon Lee; et al. 2025. Journal of the American College of Radiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Long-term impact of computer-aided adenoma detection: a prospective observational studyTaishi Okumura; Shin-ei Kudo; Yutaro Ide; et al. 2026. Endoscopyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- PDFCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Learning and deskilling effects of artificial intelligence in colonoscopy among endoscopists with different levels of experience: a pragmatic, prospective trialTom Andre Pedersen; Yuichi Mori; Edoardo Botteri; et al. 2026. Endoscopyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Cohort-aware CT-first modeling for clinically oriented pulmonary lesion characterization and prognosisJie Peng; Xian Liu; Yaping Quan; et al. 2026. npj Digital Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Using AI to Identify Unremarkable Chest Radiographs for Automatic ReportingLouis Lind Plesner; Felix C. Müller; Mathias W. Brejnebøl; et al. 2024. Radiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AIDrew Prinster; Amama Mahmood; Suchi Saria; et al. 2024. Radiologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Assessment of an Artificial Intelligence Algorithm for Detection of Intracranial HemorrhageRyan A. Rava; Samantha E. Seymour; Meredith E. LaQue; et al. 2021. World Neurosurgeryjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Clinical Evidence and FDA Recalls of Artificial Intelligence–Enabled Medical DevicesYijun Ren; Yi Zheng; Daniel Windecker; et al. 2026. JAMA Network Openjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory studyAnindo Saha; Joeran S Bosma; Jasper J Twilt; et al. 2024. The Lancet Oncologyjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populationsLaleh Seyyed-Kalantari; Haoran Zhang; Matthew B. A. McDermott; et al. 2021. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- FDA Approval of Artificial Intelligence and Machine Learning Devices in RadiologyRam Sivakumar; Brian Lue; Shinjini Kundu. 2025. JAMA Network Openjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- The effect of medical explanations from large language models on diagnostic accuracy in radiologyPhilipp Spitzer; Daniel Hendriks; Jan Rudolph; et al. 2026. npj Digital Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- AI Triage of Normal Chest Radiographs: A Silent Trial and Failure AnalysisMathew Storey; Anthony Chung; Jack Packer; et al. 2026. Radiology: Artificial Intelligencejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 UpdateAli S. Tejani; Michail E. Klontzas; Anthony A. Gatti; et al. 2024. Radiology: Artificial Intelligencejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- AI-based chest X-ray prioritization in the lung cancer diagnostic pathway: the LungIMPACT randomized controlled trialNick Woznitza; Lesley Smith; Janette Rawlinson; et al. 2026. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- The limits of fair medical imaging AI in real-world generalizationYuzhe Yang; Haoran Zhang; Judy W. Gichoya; et al. 2024. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Heterogeneity and predictors of the effects of AI assistance on radiologistsFeiyang Yu; Alex Moehring; Oishi Banerjee; et al. 2024. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trialXiaoming Zhang; Chunli Li; Xu Han; et al. 2026. Nature Medicinejournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 1); Diagnostic Imaging, Radiology, and Nuclear Medicine (passage 2)
- MedVersa: A Generalist Foundation Model for Diverse Medical Imaging TasksHong-Yu Zhou; Julián Nicolás Acosta; Subathra Adithan; et al. 2026. NEJM AIjournal articleCited in: Diagnostic Imaging, Radiology, and Nuclear Medicine
Recommended reading
- BI-RADSRecommended in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- Lung-RADSRecommended in: Diagnostic Imaging, Radiology, and Nuclear Medicine
- PI-RADSRecommended in: Diagnostic Imaging, Radiology, and Nuclear Medicine
Internal Medicine and Hospital Medicine
References
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- Abbott, February 2026Cited in: Internal Medicine and Hospital Medicine
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- ADA 2025/2026 Standards of Care Section 7Cited in: Internal Medicine and Hospital Medicine
- Patterns of AI Use in Clinical Work by Hospitalists: Survey StudyPrabhava Bagla; Jasmah Hanna; Bhargav Marthambadi; et al. 2026. Journal of Medical Internet Researchjournal articleCited in: Internal Medicine and Hospital Medicine
- Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alertsJason M Baron; Richard Huang; Dustin McEvoy; et al. 2021. JAMIA Openjournal articleCited in: Internal Medicine and Hospital Medicine
- Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 DiabetesSue A. Brown; Boris P. Kovatchev; Dan Raghinaru; et al. 2019. New England Journal of Medicinejournal articleCited in: Internal Medicine and Hospital Medicine
- Validation of a Proprietary Deterioration Index Model and Performance in Hospitalized AdultsThomas F. Byrd IV; Bronwyn Southwell; Adarsh Ravishankar; et al. 2023. JAMA Network Openjournal articleCited in: Internal Medicine and Hospital Medicine
- CDC, March 2026Cited in: Internal Medicine and Hospital Medicine
- Recommendations for Clinicians, Technologists, and Healthcare Organizations on the Use of Generative Artificial Intelligence in Medicine: A Position Statement from the Society of General Internal MedicineByron Crowe; Shreya Shah; Derek Teng; et al. 2025. Journal of General Internal Medicinejournal articleCited in: Internal Medicine and Hospital Medicine
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- FDA DEN180001Cited in: Internal Medicine and Hospital Medicine
- FDA, June 2023Cited in: Internal Medicine and Hospital Medicine
- FDA, K241676Cited in: Internal Medicine and Hospital Medicine
- FDA, P100045/S086Cited in: Internal Medicine and Hospital Medicine
- Rehospitalizations among Patients in the Medicare Fee-for-Service ProgramStephen F. Jencks; Mark V. Williams; Eric A. Coleman. 2009. New England Journal of Medicinejournal articleCited in: Internal Medicine and Hospital Medicine
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- Causal inference framework for personalised b/tsDMARD selection in rheumatoid arthritis: ANSWER cohort validationKosuke Kita; Kosuke Ebina; Yuki Suzuki; et al. 2026. npj Digital Medicinejournal articleCited in: Internal Medicine and Hospital Medicine
- The Effect of an AI-Based, Autonomous, Digital Health Intervention Using Precise Lifestyle Guidance on Blood Pressure in Adults With Hypertension: Single-Arm Nonrandomized TrialJared Leitner; Po-Han Chiang; Parag Agnihotri; et al. 2024. JMIR Cardiojournal articleCited in: Internal Medicine and Hospital Medicine
- Haemodynamic-guided management of heart failure (GUIDE-HF): a randomised controlled trialJoAnn Lindenfeld; Michael R Zile; Akshay S Desai; et al. 2021. The Lancetjournal articleCited in: Internal Medicine and Hospital Medicine
- Mathioudakis et al., 2025Cited in: Internal Medicine and Hospital Medicine
- Merai et al., 2024Cited in: Internal Medicine and Hospital Medicine
- Derivation and independent validation of kidneyintelX .dkd: A prognostic test for the assessment of diabetic kidney disease progressionGirish N. Nadkarni; Sharon Stapleton; Dipti Takale; et al. 2023. Diabetes, Obesity and Metabolismjournal articleCited in: Internal Medicine and Hospital Medicine
- Overrides of medication-related clinical decision support alerts in outpatientsKaren C Nanji; Sarah P Slight; Diane L Seger; et al. 2014. Journal of the American Medical Informatics Associationjournal articleCited in: Internal Medicine and Hospital Medicine
- Large Language Models in Medicine: The Potentials and PitfallsJesutofunmi A. Omiye; Haiwen Gui; Shawheen J. Rezaei; et al. 2024. Annals of Internal Medicinejournal articleCited in: Internal Medicine and Hospital Medicine (passage 1)Recommended in: Internal Medicine and Hospital Medicine (passage 2)
- A review of human factors principles for the design and implementation of medication safety alerts in clinical information systemsShobha Phansalkar; Judy Edworthy; Elizabeth Hellier; et al. 2010. Journal of the American Medical Informatics Associationjournal articleCited in: Internal Medicine and Hospital Medicine
- Prenosis, NEJM AI, 2024Cited in: Internal Medicine and Hospital Medicine
- Scalable and accurate deep learning with electronic health recordsAlvin Rajkomar; Eyal Oren; Kai Chen; et al. 2018. npj Digital Medicinejournal articleCited in: Internal Medicine and Hospital Medicine (passage 1); Internal Medicine and Hospital Medicine (passage 2)Recommended in: Internal Medicine and Hospital Medicine (passage 3)
- Roche, October 2025Cited in: Internal Medicine and Hospital Medicine
- Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trialSarah C. Rossetti; Patricia C. Dykes; Chris Knaplund; et al. 2025. Nature Medicinejournal articleCited in: Internal Medicine and Hospital Medicine
- Development and validation of a continuous measure of patient condition using the Electronic Medical RecordMichael J. Rothman; Steven I. Rothman; Joseph Beals IV. 2013. Journal of Biomedical Informaticsjournal articleCited in: Internal Medicine and Hospital Medicine
- Evaluation of symptom checkers for self diagnosis and triage: audit studyHannah L Semigran; Jeffrey A Linder; Courtney Gidengil; et al. 2015. BMJjournal articleCited in: Internal Medicine and Hospital Medicine
- Sendelbach & Funk, 2013Cited in: Internal Medicine and Hospital Medicine
- KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney DiseasePaul E. Stevens; Sofia B. Ahmed; Juan Jesus Carrero; et al. 2024. Kidney Internationaljournal articleCited in: Internal Medicine and Hospital Medicine
- Safety, efficacy and clinical generalization of the STAR protocol: a retrospective analysisKent W. Stewart; Christopher G. Pretty; Hamish Tomlinson; et al. 2016. Annals of Intensive Carejournal articleCited in: Internal Medicine and Hospital Medicine
- A systematic review of the impacts of remote patient monitoring (RPM) interventions on safety, adherence, quality-of-life and cost-related outcomesSi Ying Tan; Jennifer Sumner; Yuchen Wang; et al. 2024. npj Digital Medicinejournal articleCited in: Internal Medicine and Hospital Medicine
- Responsible Use of Artificial Intelligence to Improve Kidney CareNavdeep Tangri; Wisit Cheungpasitporn; Stanley D. Crittenden; et al. 2026. Journal of the American Society of Nephrologyjournal articleCited in: Internal Medicine and Hospital Medicine (passage 1); Internal Medicine and Hospital Medicine (passage 2)
- Validation of the Klinrisk Machine Learning Model for CKD Progression in a Large Representative US PopulationNavdeep Tangri; Thomas W. Ferguson; Chia-Chen Teng; et al. 2026. Journal of the American Society of Nephrologyjournal articleCited in: Internal Medicine and Hospital Medicine
- Effect of artificial pancreas systems on glycaemic control in patients with type 1 diabetes: a systematic review and meta-analysis of outpatient randomised controlled trialsAlanna Weisman; Johnny-Wei Bai; Marina Cardinez; et al. 2017. The Lancet Diabetes & Endocrinologyjournal articleCited in: Internal Medicine and Hospital Medicine
- Whelan et al., 2024Cited in: Internal Medicine and Hospital Medicine
- Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trialRisa M. Wolf; Roomasa Channa; T. Y. Alvin Liu; et al. 2024. Nature Communicationsjournal articleCited in: Internal Medicine and Hospital Medicine
- Wong et al., 2021Cited in: Internal Medicine and Hospital Medicine (passage 1); Internal Medicine and Hospital Medicine (passage 2)Recommended in: Internal Medicine and Hospital Medicine (passage 3)
- Real-Time AI-Assisted Insulin Titration System for Glucose Control in Patients With Type 2 DiabetesZhen Ying; Yujuan Fan; Congling Chen; et al. 2025. JAMA Network Openjournal articleCited in: Internal Medicine and Hospital Medicine
- Technological functionality and system architecture of mobile health interventions for diabetes management: a systematic review and meta-analysis of randomized controlled trialsXinran Yu; Yifeng Wang; Zhengyang Liu; et al. 2025. Frontiers in Public Healthjournal articleCited in: Internal Medicine and Hospital Medicine
Recommended reading
- AI Resource HubRecommended in: Internal Medicine and Hospital Medicine (passage 1); Internal Medicine and Hospital Medicine (passage 2)
- Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a RealityDavid W. Bates; Gilad J. Kuperman; Samuel Wang; et al. 2003. Journal of the American Medical Informatics Associationjournal articleRecommended in: Internal Medicine and Hospital Medicine
- Artificial Intelligence–Assisted Colonoscopy for Polyp DetectionSaeed Soleymanjahi; Jack Huebner; Lina Elmansy; et al. 2024. Annals of Internal Medicinejournal articleRecommended in: Internal Medicine and Hospital Medicine
Surgery, Anesthesiology, and Perioperative Care
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- PhysSFI-Net: physics-informed geometric learning of skeletal and facial interactions for orthognathic surgical outcome predictionJiahao Bao; Huazhen Liu; Yu Zhuang; et al. 2026. npj Digital Medicinejournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
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- Cost analyses in randomized trials on robot-assisted surgery: systematic reviewSterre R J Bosscha; Rawin Amiri; Faridi Jamaludin; et al. 2025. BJS Openjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
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- FDA Summary of Safety and EffectivenessCited in: Surgery, Anesthesiology, and Perioperative Care
- FDA user manual P250008Cited in: Surgery, Anesthesiology, and Perioperative Care
- Robotic vs Laparoscopic Surgery for Middle and Low Rectal CancerQingyang Feng; Weitang Yuan; Taiyuan Li; et al. 2025. JAMAjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Artificial Intelligence in Surgery: Promises and PerilsDaniel A. Hashimoto; Guy Rosman; Daniela Rus; et al. 2018. Annals of Surgeryjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
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- A randomized controlled trial of artificial intelligence-based analytics for clinical deteriorationJessica Keim-Malpass; Sarah J. Ratcliffe; Matthew T. Clark; et al. 2026. Scientific Reportsjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care (passage 1); Surgery, Anesthesiology, and Perioperative Care (passage 2)
- SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learningJi Woong (Brian) Kim; Juo-Tung Chen; Pascal Hansen; et al. 2025. Science Roboticsjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Automation of surgical skill assessment using a three-stage machine learning algorithmJoël L. Lavanchy; Joel Zindel; Kadir Kirtac; et al. 2021. Scientific Reportsjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Surgical embodied intelligence for generalized task autonomy in laparoscopic robot-assisted surgeryYonghao Long; Anran Lin; Derek Hang Chun Kwok; et al. 2025. Science Roboticsjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoringHani J. Marcus; Pedro T. Ramirez; Danyal Z. Khan; et al. 2024. Nature Medicinejournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
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- Comparative Safety of Robotic-Assisted vs Laparoscopic Cholecystectomy in Contemporary PracticeCody Lendon Mullens; Eunice Y. Lee; Jyothi R. Thumma; et al. 2026. JAMA Surgeryjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studiesMyura Nagendran; Yang Chen; Christopher A Lovejoy; et al. 2020. BMJjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- MySurgeryRisk Model Predictions of Postoperative Complications and MortalityYuanfang Ren; Esra Adiyeke; Ziyuan Guan; et al. 2026. JAMA Surgeryjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care (passage 1); Surgery, Anesthesiology, and Perioperative Care (passage 2)
- Privacy-Preserving Surgical Video Analysis with Swarm Learning — Results from a Multinational Appendectomy CohortOliver Lester Saldanha; Kevin Pfeiffer; Sebastian Bodenstedt; et al. 2026. NEJM AIjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Receipt of Industry Payments and Surgeons’ Adoption of Robotic-Assisted SurgeryWei San Loh; Edward C. Norton; Jyothi Thumma; et al. 2026. JAMA Network Openjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care (passage 1); Surgery, Anesthesiology, and Perioperative Care (passage 2)
- Effect of a Machine Learning-Derived Early Warning Tool With Treatment Protocol on Hypotension During Cardiac Surgery and ICU Stay: The Hypotension Prediction 2 (HYPE-2) Randomized Clinical TrialJaap Schuurmans; Santino R. Rellum; Jimmy Schenk; et al. 2025. Critical Care Medicinejournal articleCited in: Surgery, Anesthesiology, and Perioperative Care (passage 1); Surgery, Anesthesiology, and Perioperative Care (passage 2)
- Machine learning model-guided selective use of temporary diverting ileostomy in rectal cancer surgery: a randomized controlled trialShengli Shao; Yanqi Li; Jianghao Li; et al. 2026. Nature Communicationsjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care (passage 1); Surgery, Anesthesiology, and Perioperative Care (passage 2)
- Expectations vs Reality of an Intraoperative Artificial Intelligence InterventionMelissa Thornton; Benjamin A. Y. Cher; Cameron Macdonald; et al. 2026. JAMA Surgeryjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care (passage 1); Surgery, Anesthesiology, and Perioperative Care (passage 2)
- EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic VideosAndru P. Twinanda; Sherif Shehata; Didier Mutter; et al. 2017. IEEE Transactions on Medical Imagingjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Machine learning algorithm validation with a limited sample sizeAndrius Vabalas; Emma Gowen; Ellen Poliakoff; et al. 2019. PLOS ONEjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Clinical Outcomes of Laparoscopic vs Robotic-Assisted Cholecystectomy in Acute Care SurgeryNathnael Abera Woldehana; Andrew Jung; Brett Colton Parker; et al. 2025. JAMA Surgeryjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
- Wong et al., 2021Cited in: Surgery, Anesthesiology, and Perioperative Care
- Artificial Intelligence–Driven Video-Based Surgical Skill Assessment in Hepatobiliary Laparoscopic SurgeryXiaojun Zeng; Yuchong Li; Junfeng Wang; et al. 2026. JAMA Surgeryjournal articleCited in: Surgery, Anesthesiology, and Perioperative Care
Recommended reading
Pediatrics and Neonatology
References
- Association of Use of the Neonatal Early-Onset Sepsis Calculator With Reduction in Antibiotic Therapy and SafetyNiek B. Achten; Claus Klingenberg; William E. Benitz; et al. 2019. JAMA Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Racial–Ethnic Disparities in Diabetes Technology use Among Young Adults with Type 1 DiabetesShivani Agarwal; Clyde Schechter; Jeffrey Gonzalez; et al. 2021. Diabetes Technology & Therapeuticsjournal articleCited in: Pediatrics and Neonatology
- The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemiaDaniel A. Arber; Attilio Orazi; Robert Hasserjian; et al. 2016. Bloodjournal articleCited in: Pediatrics and Neonatology
- A Randomized Trial of Closed-Loop Control in Children with Type 1 DiabetesMarc D. Breton; Lauren G. Kanapka; Roy W. Beck; et al. 2020. New England Journal of Medicinejournal articleCited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
- Brewster et al., JAMA Pediatrics, 2025Cited in: Pediatrics and Neonatology
- Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural NetworksJames M. Brown; J. Peter Campbell; Andrew Beers; et al. 2018. JAMA Ophthalmologyjournal articleCited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
- The effect of an electronic monitoring device with audiovisual reminder function on adherence to inhaled corticosteroids and school attendance in children with asthma: a randomised controlled trialAmy H Y Chan; Alistair W Stewart; Jeff Harrison; et al. 2015. The Lancet Respiratory Medicinejournal articleCited in: Pediatrics and Neonatology
- Deep Learning for the Diagnosis of Stage in Retinopathy of PrematurityJimmy S. Chen; Aaron S. Coyner; Susan Ostmo; et al. 2021. Ophthalmology Retinajournal articleCited in: Pediatrics and Neonatology
- Multicenter Crossover Study of Automated Control of Inspired Oxygen in Ventilated Preterm InfantsNelson Claure; Eduardo Bancalari; Carmen D'Ugard; et al. 2011. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Closed-loop control of inspired oxygen in premature infantsNelson Claure; Eduardo Bancalari. 2015. Seminars in Fetal and Neonatal Medicinejournal articleCited in: Pediatrics and Neonatology
- Health-Related Quality of Life and Treatment Satisfaction in Parents and Children with Type 1 Diabetes Using Closed-Loop ControlErin C. Cobry; Lauren G. Kanapka; Eda Cengiz; et al. 2021. Diabetes Technology & Therapeuticsjournal articleCited in: Pediatrics and Neonatology
- The International Neuroblastoma Risk Group (INRG) Classification System: An INRG Task Force ReportSusan L. Cohn; Andrew D.J. Pearson; Wendy B. London; et al. 2009. Journal of Clinical Oncologyjournal articleCited in: Pediatrics and Neonatology
- Timing of the Diagnosis of Autism in African American ChildrenJohn N. Constantino; Anna M. Abbacchi; Celine Saulnier; et al. 2020. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Automated identification of implausible values in growth data from pediatric electronic health recordsCarrie Daymont; Michelle E Ross; A Russell Localio; et al. 2017. Journal of the American Medical Informatics Associationjournal articleCited in: Pediatrics and Neonatology
- Dewey et al., 1992Cited in: Pediatrics and Neonatology
- Assessing ADHD symptoms in children and adults: evaluating the role of objective measuresTheresa S. Emser; Blair A. Johnston; J. Douglas Steele; et al. 2018. Behavioral and Brain Functionsjournal articleCited in: Pediatrics and Neonatology
- FDA De Novo DEN200069Cited in: Pediatrics and Neonatology
- FDA DEN200069Cited in: Pediatrics and Neonatology
- FDA K021230Cited in: Pediatrics and Neonatology
- FDA K243558Cited in: Pediatrics and Neonatology
- FDA, 2023Cited in: Pediatrics and Neonatology
- Screening Examination of Premature Infants for Retinopathy of PrematurityWalter M. Fierson; AMERICAN ACADEMY OF PEDIATRICS Section on Ophthalmology; AMERICAN ACADEMY OF OPHTHALMOLOGY; et al. 2018. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Machine learning approaches to personalize early prediction of asthma exacerbationsJoseph Finkelstein; In cheol Jeong. 2017. Annals of the New York Academy of Sciencesjournal articleCited in: Pediatrics and Neonatology
- A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice*Heather M. Giannini; Jennifer C. Ginestra; Corey Chivers; et al. 2019. Critical Care Medicinejournal articleCited in: Pediatrics and Neonatology
- International pediatric sepsis consensus conference: Definitions for sepsis and organ dysfunction in pediatrics*Brahm Goldstein; Brett Giroir; Adrienne Randolph. 2005. Pediatric Critical Care Medicinejournal articleCited in: Pediatrics and Neonatology
- Machine Learning–Based Prediction of Clinical Outcomes for Children During Emergency Department TriageTadahiro Goto; Carlos A. Camargo Jr; Mohammad Kamal Faridi; et al. 2019. JAMA Network Openjournal articleCited in: Pediatrics and Neonatology
- Evaluation of artificial intelligence-based telemedicine screening for retinopathy of prematurityMiles F. Greenwald; Ian D. Danford; Malika Shahrawat; et al. 2020. Journal of American Association for Pediatric Ophthalmology and Strabismusjournal articleCited in: Pediatrics and Neonatology
- Retinopathy of prematurityAnn Hellström; Lois EH Smith; Olaf Dammann. 2013. The Lancetjournal articleCited in: Pediatrics and Neonatology
- Challenges of Accurately Measuring and Using BMI and Other Indicators of Obesity in ChildrenJohn H. Himes. 2009. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Large Language Models Using Clinical Text in PediatricsTracy Huang. 2026. JAMA Network Openjournal articleCited in: Pediatrics and Neonatology
- Toward Trustworthy Pediatric AI: A Call to Action From the National Academy of MedicineKevin B. Johnson; Mark Simonian; Laura L. Adams; et al. 2025. Pediatricsjournal articleCited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
- Screening in toddlers and preschoolers at risk for autism spectrum disorder: Evaluating a novel mobile‐health screening toolStephen M. Kanne; Laura Arnstein Carpenter; Zachary Warren. 2018. Autism Researchjournal articleCited in: Pediatrics and Neonatology
- Clinical Practice Guideline Revision: Management of Hyperbilirubinemia in the Newborn Infant 35 or More Weeks of GestationAlex R. Kemper; Thomas B. Newman; Jonathan L. Slaughter; et al. 2022. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networksD.H. Kim; T. MacKinnon. 2018. Clinical Radiologyjournal articleCited in: Pediatrics and Neonatology
- Continuous vital sign analysis for predicting and preventing neonatal diseases in the twenty-first century: big data to the forefrontNavin Kumar; Gangaram Akangire; Brynne Sullivan; et al. 2020. Pediatric Researchjournal articleCited in: Pediatrics and Neonatology
- Epidemiology of neonatal encephalopathy and hypoxic–ischaemic encephalopathyJennifer J. Kurinczuk; Melanie White-Koning; Nadia Badawi. 2010. Early Human Developmentjournal articleCited in: Pediatrics and Neonatology
- A Quantitative, Risk-Based Approach to the Management of Neonatal Early-Onset SepsisMichael W. Kuzniewicz; Karen M. Puopolo; Allen Fischer; et al. 2017. JAMA Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Automated control of inspired oxygen in ventilated preterm infants: crossover physiological studyMithilesh Lal; Win Tin; Sunil Sinha. 2015. Acta Paediatricajournal articleCited in: Pediatrics and Neonatology
- Promoting Optimal Development: Identifying Infants and Young Children With Developmental Disorders Through Developmental Surveillance and ScreeningPaul H. Lipkin; Michelle M. Macias; COUNCIL ON CHILDREN WITH DISABILITIES, SECTION ON DEVELOPMENTAL AND BEHAVIORAL PEDIATRICS; et al. 2020. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Predicting motor outcome and death in term hypoxic-ischemic encephalopathyM. Martinez-Biarge; J. Diez-Sebastian; O. Kapellou; et al. 2011. Neurologyjournal articleCited in: Pediatrics and Neonatology
- Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record dataAaron J. Masino; Mary Catherine Harris; Daniel Forsyth; et al. 2019. PLOS ONEjournal articleCited in: Pediatrics and Neonatology
- Update to the Neonatal Early-Onset Sepsis Calculator Utilizing a Contemporary CohortMichael W. Kuzniewicz. 2024. PediatricsCited in: Pediatrics and Neonatology
- Mortality Reduction by Heart Rate Characteristic Monitoring in Very Low Birth Weight Neonates: A Randomized TrialJoseph Randall Moorman; Waldemar A. Carlo; John Kattwinkel; et al. 2011. The Journal of Pediatricsjournal articleCited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
- Recommendations for the use of pediatric data in artificial intelligence and machine learning ACCEPT-AIV. Muralidharan. 2023. npj Digital Medicinejournal articleCited in: Pediatrics and Neonatology
- Early EEG Grade and Outcome at 5 Years After Mild Neonatal Hypoxic Ischemic EncephalopathyDeirdre M. Murray; Catherine M. O’Connor; C. Anthony Ryan; et al. 2016. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
- Effect of a Pediatric Early Warning System on All-Cause Mortality in Hospitalized Pediatric PatientsChristopher S. Parshuram; Karen Dryden-Palmer; Catherine Farrell; et al. 2018. JAMAjournal articleCited in: Pediatrics and Neonatology
- A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trialAndreea M Pavel; Janet M Rennie; Linda S de Vries; et al. 2020. The Lancet Child & Adolescent Healthjournal articleCited in: Pediatrics and Neonatology
- Sensitivity of the Kaiser Permanente early-onset sepsis calculator: A systematic review and meta-analysisKatherine J. Pettinger. 2020. EClinicalMedicinejournal articleCited in: Pediatrics and Neonatology
- AI-guided precision parenteral nutrition for neonatal intensive care unitsThanaphong Phongpreecha; Marc Ghanem; Jonathan D. Reiss; et al. 2025. Nature Medicinejournal articleCited in: Pediatrics and Neonatology
- Pinsker et al., 2020Cited in: Pediatrics and Neonatology
- Estimating the Probability of Neonatal Early-Onset Infection on the Basis of Maternal Risk FactorsKaren M. Puopolo; David Draper; Soora Wi; et al. 2011. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Management of Neonates Born at ≤34 6/7 Weeks’ Gestation With Suspected or Proven Early-Onset Bacterial SepsisKaren M. Puopolo; William E. Benitz; Theoklis E. Zaoutis; et al. 2018. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Management of Neonates Born at ≥35 0/7 Weeks’ Gestation With Suspected or Proven Early-Onset Bacterial SepsisKaren M. Puopolo; William E. Benitz; Theoklis E. Zaoutis; et al. 2018. Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Ensuring Fairness in Machine Learning to Advance Health EquityAlvin Rajkomar; Michaela Hardt; Michael D. Howell; et al. 2018. Annals of Internal Medicinejournal articleCited in: Pediatrics and Neonatology
- Binomial Classification of Pediatric Elbow Fractures Using a Deep Learning Multiview Approach Emulating Radiologist Decision MakingJesse C. Rayan; Nakul Reddy; J. Herman Kan; et al. 2019. Radiology: Artificial Intelligencejournal articleCited in: Pediatrics and Neonatology
- Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurityTravis K Redd; John Peter Campbell; James M Brown; et al. 2019. British Journal of Ophthalmologyjournal articleCited in: Pediatrics and Neonatology
- Racial Bias in Pulse Oximetry MeasurementMichael W. Sjoding; Robert P. Dickson; Theodore J. Iwashyna; et al. 2020. New England Journal of Medicinejournal articleCited in: Pediatrics and Neonatology
- Automated oxygen delivery for preterm infants with respiratory dysfunctionIsabella G Stafford; Nai Ming Lai; Kenneth Tan. 2023. Cochrane Database of Systematic Reviewsjournal articleCited in: Pediatrics and Neonatology
- Stenson et al., 2013Cited in: Pediatrics and Neonatology
- Machine learning for suicide risk prediction in children and adolescents with electronic health recordsChang Su; Robert Aseltine; Riddhi Doshi; et al. 2020. Translational Psychiatryjournal articleCited in: Pediatrics and Neonatology
- Tampu et al., 2025Cited in: Pediatrics and Neonatology
- Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep LearningStanford Taylor; James M. Brown; Kishan Gupta; et al. 2019. JAMA Ophthalmologyjournal articleCited in: Pediatrics and Neonatology
- Toh et al., 2019Cited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
- Safety and effectiveness of the early-onset sepsis calculator to reduce antibiotic exposure in at-risk newborns: a cluster-randomised controlled trialBo M. van der Weijden; Sanne W.C.M. Janssen; Marijke C. van der Weide; et al. 2025. eClinicalMedicinejournal articleCited in: Pediatrics and Neonatology
- Automated versus Manual Oxygen Control with Different Saturation Targets and Modes of Respiratory Support in Preterm InfantsAnton H. van Kaam; Helmut D. Hummler; Maria Wilinska; et al. 2015. The Journal of Pediatricsjournal articleCited in: Pediatrics and Neonatology
- Trial of Hybrid Closed-Loop Control in Young Children with Type 1 DiabetesR. Paul Wadwa; Zachariah W. Reed; Bruce A. Buckingham; et al. 2023. New England Journal of Medicinejournal articleCited in: Pediatrics and Neonatology
- Predicting Risk of Suicide Attempts Over Time Through Machine LearningColin G. Walsh; Jessica D. Ribeiro; Joseph C. Franklin. 2017. Clinical Psychological Sciencejournal articleCited in: Pediatrics and Neonatology
- Evaluating retrieval-augmented large language models for pediatric cardiology knowledge using standardized questionsJacob Weiser; John K. Triedman; Joshua Mayourian. 2026. npj Digital Medicinejournal articleCited in: Pediatrics and Neonatology
- Identification of Pediatric Sepsis for Epidemiologic Surveillance Using Electronic Clinical Data*Scott L. Weiss; Fran Balamuth; Marianne Chilutti; et al. 2020. Pediatric Critical Care Medicinejournal articleCited in: Pediatrics and Neonatology
- Biomarkers in neonatal encephalopathy: new approaches and ongoing questionsCourtney J. Wusthoff. 2022. Pediatric Researchjournal articleCited in: Pediatrics and Neonatology
- A randomised controlled trial of an automated oxygen delivery algorithm for preterm neonates receiving supplemental oxygen without mechanical ventilationJames Zapata; John Jairo Gómez; Robinson Araque Campo; et al. 2014. Acta Paediatricajournal articleCited in: Pediatrics and Neonatology
- Uncertainty-inspired open-set model for identifying infantile fundus abnormalitiesXinyu Zhao; Zhenquan Wu; Xuemei Zhu; et al. 2026. npj Digital Medicinejournal articleCited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
- Trajectories of the heart rate characteristics index, a physiomarker of sepsis in premature infants, predict Neonatal ICU mortalityAmanda M Zimmet; Brynne A Sullivan; J Randall Moorman; et al. 2020. JRSM Cardiovascular Diseasejournal articleCited in: Pediatrics and Neonatology
- Racial, Ethnic, and Language Disparities in Early Childhood Developmental/Behavioral EvaluationsKatharine E. Zuckerman; Kimber M. Mattox; Brianna K. Sinche; et al. 2014. Clinical Pediatricsjournal articleCited in: Pediatrics and Neonatology (passage 1); Pediatrics and Neonatology (passage 2)
Obstetrics and Gynecology
References
- ACOG and SMFM, 2021Cited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2)
- ACOG Committee Opinion 664Cited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2)
- ACOG Committee Opinion 819Cited in: Obstetrics and Gynecology
- ACOG, 2026Cited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2); Obstetrics and Gynecology (passage 3)
- African AVE Collaborative, Lancet Glob Health, 2026Cited in: Obstetrics and Gynecology
- Menstrual Cycle Variations in Wearable-Detected Finger Temperature and Heart Rate, But Not in Sleep Metrics, in Young and Midlife IndividualsElisabet Alzueta; Marie Gombert-Labedens; Harold Javitz; et al. 2024. Journal of Biological Rhythmsjournal articleCited in: Obstetrics and Gynecology
- Prediction of preterm birth in nulliparous women using logistic regression and machine learningReza Arabi Belaghi; Joseph Beyene; Sarah D. McDonald. 2021. PLOS ONEjournal articleCited in: Obstetrics and Gynecology
- AI-assisted cervical cytology precancerous screening for high-risk population in resource-limited regions using a compact microscopeJiaxin Bai; Ning Li; Hua Ye; et al. 2025. Nature Communicationsjournal articleCited in: Obstetrics and Gynecology
- Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational studyHeling Bao; Hui Bi; Xiaosong Zhang; et al. 2020. Gynecologic Oncologyjournal articleCited in: Obstetrics and Gynecology
- SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand UltrasoundChristian F. Baumgartner; Konstantinos Kamnitsas; Jacqueline Matthew; et al. 2017. IEEE Transactions on Medical Imagingjournal articleCited in: Obstetrics and Gynecology
- Does timing matter—meta-analysis and systematic review of digital cognitive behavioral therapy in the perinatal periodCathelijne Bijen; Stephanie Homan; Marta A. Marciniak. 2026. npj Digital Medicinejournal articleCited in: Obstetrics and Gynecology
- Interobserver and intraobserver reliability of the NICHD 3-Tier Fetal Heart Rate Interpretation SystemSean C. Blackwell; William A. Grobman; Leah Antoniewicz; et al. 2011. American Journal of Obstetrics and Gynecologyjournal articleCited in: Obstetrics and Gynecology
- Validation of American College of Radiology Ovarian-Adnexal Reporting and Data System Ultrasound (O-RADS US): Analysis on 1054 adnexal massesLan Cao; Mingjie Wei; Ying Liu; et al. 2021. Gynecologic Oncologyjournal articleCited in: Obstetrics and Gynecology
- CDC/NCHS, 2026Cited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2); Obstetrics and Gynecology (passage 3)
- Clinical Practice Guideline 10Cited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2)
- Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessmentZafer Cömert; Adnan Fatih Kocamaz; Velappan Subha. 2018. Computers in Biology and Medicinejournal articleCited in: Obstetrics and Gynecology
- Racial and ethnic differences in primary, unscheduled cesarean deliveries among low-risk primiparous women at an academic medical center: a retrospective cohort studyJoyce K Edmonds; Revital Yehezkel; Xun Liao; et al. 2013. BMC Pregnancy and Childbirthjournal articleCited in: Obstetrics and Gynecology
- FDA K242342Cited in: Obstetrics and Gynecology
- FDA, DEN250007Cited in: Obstetrics and Gynecology
- Antenatal cardiotocography for fetal assessmentRosalie M Grivell; Zarko Alfirevic; Gillian ML Gyte; et al. 2015. Cochrane Database of Systematic Reviewsjournal articleCited in: Obstetrics and Gynecology
- An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer ScreeningLiming Hu; David Bell; Sameer Antani; et al. 2019. JNCI: Journal of the National Cancer Institutejournal articleCited in: Obstetrics and Gynecology
- Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trialPeter J. Illingworth; Christos Venetis; David K. Gardner; et al. 2024. Nature Medicinejournal articleCited in: Obstetrics and Gynecology
- INFANT Collaborative Group, 2017Cited in: Obstetrics and Gynecology
- K243614Cited in: Obstetrics and Gynecology
- K252148Cited in: Obstetrics and Gynecology
- Clinical outcomes of uninterrupted embryo culture with or without time-lapse-based embryo selection versus interrupted standard culture (SelecTIMO): a three-armed, multicentre, double-blind, randomised controlled trialD C Kieslinger; C G Vergouw; L Ramos; et al. 2023. The Lancetjournal articleCited in: Obstetrics and Gynecology
- Development and performance evaluation of an artificial intelligence algorithm using cell-free DNA fragment distance for non-invasive prenatal testing (aiD-NIPT)Junnam Lee; Sae-Mi Lee; Jin Mo Ahn; et al. 2022. Frontiers in Geneticsjournal articleCited in: Obstetrics and Gynecology
- Liu et al., eClinicalMedicine, 2024Cited in: Obstetrics and Gynecology
- March of Dimes, 2015Cited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2)
- Births in the United States, 2021Joyce Martin; Brady Hamilton; Michelle Osterman. 2022reportCited in: Obstetrics and Gynecology
- Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic reviewF. Moro; M. T. Giudice; M. Ciancia; et al. 2025. Ultrasound in Obstetrics & Gynecologyjournal articleCited in: Obstetrics and Gynecology
- Learning-based prediction of gestational age from ultrasound images of the fetal brainAna I.L. Namburete; Richard V. Stebbing; Bryn Kemp; et al. 2015. Medical Image Analysisjournal articleCited in: Obstetrics and Gynecology
- Cell-free DNA Analysis for Noninvasive Examination of TrisomyMary E. Norton; Bo Jacobsson; Geeta K. Swamy; et al. 2015. New England Journal of Medicinejournal articleCited in: Obstetrics and Gynecology
- Aspirin versus Placebo in Pregnancies at High Risk for Preterm PreeclampsiaDaniel L. Rolnik; David Wright; Liona C. Poon; et al. 2017. New England Journal of Medicinejournal articleCited in: Obstetrics and Gynecology (passage 1); Obstetrics and Gynecology (passage 2)
- Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic ReviewZahra Sharifi-Heris; Juho Laitala; Antti Airola; et al. 2022. JMIR Medical Informaticsjournal articleCited in: Obstetrics and Gynecology
- Racial Bias in Pulse Oximetry MeasurementMichael W. Sjoding; Robert P. Dickson; Theodore J. Iwashyna; et al. 2020. New England Journal of Medicinejournal articleCited in: Obstetrics and Gynecology
- SMFM, 2025Cited in: Obstetrics and Gynecology
- Ethnicity and the need for correction of biochemical and ultrasound markers of chromosomal anomalies in the first trimester: a study of Oriental, Asian and Afro-Caribbean populationsK. Spencer; V. Heath; A. El-Sheikhah; et al. 2005. Prenatal Diagnosisjournal articleCited in: Obstetrics and Gynecology
- Screening for pre‐eclampsia by maternal factors and biomarkers at 11–13 weeks' gestationM. Y. Tan; A. Syngelaki; L. C. Poon; et al. 2018. Ultrasound in Obstetrics & Gynecologyjournal articleCited in: Obstetrics and Gynecology
- Oura Ring as a Tool for Ovulation Detection: Validation AnalysisNina Thigpen; Shyamal Patel; Xi Zhang. 2025. Journal of Medical Internet Researchjournal articleCited in: Obstetrics and Gynecology
- Ultrasound AI, PAIR Study, Journal of Maternal-Fetal & Neonatal Medicine, 2025Cited in: Obstetrics and Gynecology
- USPSTF, 2018Cited in: Obstetrics and Gynecology
- Machine Learning and Statistical Models to Predict Postpartum HemorrhageKartik K. Venkatesh; Robert A. Strauss; Chad A. Grotegut; et al. 2020. Obstetrics & Gynecologyjournal articleCited in: Obstetrics and Gynecology
- Deep learning-assisted versus manual reading in routine cervical cytopathology: a multicentre randomised crossover trialPeng Xue; Hongping Tang; Haiyan Weng; et al. 2026. npj Digital Medicinejournal articleCited in: Obstetrics and Gynecology
- A Deep Learning Mammography-based Model for Improved Breast Cancer Risk PredictionAdam Yala; Constance Lehman; Tal Schuster; et al. 2019. Radiologyjournal articleCited in: Obstetrics and Gynecology
- DeepFHR: intelligent prediction of fetal Acidemia using fetal heart rate signals based on convolutional neural networkZhidong Zhao; Yanjun Deng; Yang Zhang; et al. 2019. BMC Medical Informatics and Decision Makingjournal articleCited in: Obstetrics and Gynecology
Recommended reading
- acog.orgRecommended in: Obstetrics and Gynecology
- smfm.orgRecommended in: Obstetrics and Gynecology
Emergency Medicine
References
- Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integrationMohammad R. Arbabshirani; Brandon K. Fornwalt; Gino J. Mongelluzzo; et al. 2018. npj Digital Medicinejournal articleCited in: Emergency Medicine
- Medical Record Abstraction for Quality Improvement in Sepsis Care Using Artificial IntelligenceAaron Boussina; Claire Allison; Kimberly Quintero; et al. 2026. JAMA Network Openjournal articleCited in: Emergency Medicine
- Performance of a large language model on the reasoning tasks of a physicianPeter G. Brodeur; Thomas A. Buckley; Zahir Kanjee; et al. 2026. Sciencejournal articleCited in: Emergency Medicine
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triageMingyang Chen; Yijin Wu; Jiayi Ma; et al. 2026. npj Digital Medicinejournal articleCited in: Emergency Medicine (passage 1); Emergency Medicine (passage 2)
- Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock 2021Laura Evans; Andrew Rhodes; Waleed Alhazzani; et al. 2021. Critical Care Medicinejournal articleCited in: Emergency Medicine (passage 1); Emergency Medicine (passage 2)
- FDA K252970 recordCited in: Emergency Medicine
- Viz.ai Implementation of Stroke Augmented Intelligence and Communications Platform to Improve Indicators and Outcomes for a Comprehensive Stroke Center and NetworkM.E. Figurelle; D.M. Meyer; E.S. Perrinez; et al. 2023. American Journal of Neuroradiologyjournal articleCited in: Emergency Medicine
- Halici et al., 2026Cited in: Emergency Medicine
- Prospective evaluation of a large language model clinical decision support system in the emergency departmentLiron Leibovitch; Adi Ahituv; Alon Gorenshtein; et al. 2026. Nature Medicinejournal articleCited in: Emergency Medicine
- AI-enabled electrocardiogram alert for potassium imbalance treatment: a pragmatic randomized controlled trialChin Lin; Chin-Sheng Lin; Sy-Jou Chen; et al. 2026. Nature Communicationsjournal articleCited in: Emergency Medicine
- Lin et al., 2025Cited in: Emergency Medicine
- Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment TimesJuan Carlos Martinez-Gutierrez; Youngran Kim; Sergio Salazar-Marioni; et al. 2023. JAMA Neurologyjournal articleCited in: Emergency Medicine (passage 1); Emergency Medicine (passage 2)
- Mehandru et al., 2025, preprintCited in: Emergency Medicine
- Large-Scale Assessment of a Smartwatch to Identify Atrial FibrillationMarco V. Perez; Kenneth W. Mahaffey; Haley Hedlin; et al. 2019. New England Journal of Medicinejournal articleCited in: Emergency Medicine
- ChatGPT Health performance in a structured test of triage recommendationsAshwin Ramaswamy; Alvira Tyagi; Hannah Hugo; et al. 2026. Nature Medicinejournal articleCited in: Emergency Medicine (passage 1); Emergency Medicine (passage 2)
- Sarhan et al., 2025Cited in: Emergency Medicine
- SCCM 2024 Guidelines on Adult ICU DesignCited in: Emergency Medicine
- A large language model-driven multidisciplinary AI agent system predicts delirium in emergency critically ill patientsWen Shang; Tongyue Shi; Qingbian Ma; et al. 2026. Cell Reports Medicinejournal articleCited in: Emergency Medicine (passage 1); Emergency Medicine (passage 2)
- ED-triage-agent: a multi-agent framework for human-in-the-loop emergency triageKarthick Sharma; Harikrishnan Sivadas; Christopher Edwards; et al. 2026. International Journal of Medical Informaticsjournal articleCited in: Emergency Medicine
- Prehospital Injury Severity Estimate (PHISE) matches in-hospital trauma scores when embedded in AI modelsManuel Sigle; Andreas Goldschmied; Meinrad Gawaz; et al. 2026. npj Digital Medicinejournal articleCited in: Emergency Medicine
- Impact of Artificial Intelligence–Based Triage Decision Support on Emergency Department CareR. Andrew Taylor; Chris Chmura; Jeremiah Hinson; et al. 2025. NEJM AIjournal articleCited in: Emergency Medicine
- Wong et al., 2021Cited in: Emergency Medicine (passage 1); Emergency Medicine (passage 2); Emergency Medicine (passage 3)
- The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studiesNayeon Yi; Dain Baik; Gumhee Baek. 2025. Journal of Nursing Scholarshipjournal articleCited in: Emergency Medicine
Recommended reading
- official PDFRecommended in: Emergency Medicine
- Artificial Intelligence in Emergency Medicine: A Primer for the NonexpertMoira E. Smith; C. Christopher Zalesky; Sangil Lee; et al. 2025. JACEP Openjournal articleRecommended in: Emergency Medicine
- Statement of Principles on Artificial IntelligenceRecommended in: Emergency Medicine
Critical Care and Pulmonary Medicine
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- Effectiveness, safety and efficacy of INTELLiVENT–adaptive support ventilation, a closed–loop ventilation mode for use in ICU patients – a systematic reviewM. Botta; E.F.E. Wenstedt; A.M. Tsonas; et al. 2021. Expert Review of Respiratory Medicinejournal articleCited in: Critical Care and Pulmonary Medicine
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- K253092Cited in: Critical Care and Pulmonary Medicine
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Hematology-Oncology and Precision Medicine
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- FDA K251474Cited in: Hematology-Oncology and Precision Medicine
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- Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trialJessie Gommers; Veronica Hernström; Viktoria Josefsson; et al. 2026. The Lancetjournal articleCited in: Hematology-Oncology and Precision Medicine (passage 1); Hematology-Oncology and Precision Medicine (passage 2)
- Molecular residual disease and efficacy of adjuvant chemotherapy in patients with colorectal cancerDaisuke Kotani; Eiji Oki; Yoshiaki Nakamura; et al. 2023. Nature Medicinejournal articleCited in: Hematology-Oncology and Precision Medicine
- Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy studyKristina Lång; Viktoria Josefsson; Anna-Maria Larsson; et al. 2023. The Lancet Oncologyjournal articleCited in: Hematology-Oncology and Precision Medicine
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- Morris et al., 2024, meeting abstractCited in: Hematology-Oncology and Precision Medicine (passage 1); Hematology-Oncology and Precision Medicine (passage 2)
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- ctDNA-Guided Adjuvant Atezolizumab in Muscle-Invasive Bladder CancerThomas Powles; Ariel G. Kann; Daniel Castellano; et al. 2025. New England Journal of Medicinejournal articleCited in: Hematology-Oncology and Precision Medicine
- Principles for the Responsible Use of AI in OncologyCited in: Hematology-Oncology and Precision Medicine
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- Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast CancerJoseph A. Sparano; Robert J. Gray; Della F. Makower; et al. 2018. New England Journal of Medicinejournal articleCited in: Hematology-Oncology and Precision Medicine
- Role prompting modulates linguistic style but not clinical decision structure in GPT-5 tumour board simulationDerna Stifini; Andrea Della Penna; André L. Mihaljevic; et al. 2026. npj Digital Medicinejournal articleCited in: Hematology-Oncology and Precision Medicine
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- Circulating tumor DNA-guided adjuvant therapy in locally advanced colon cancer: the randomized phase 2/3 DYNAMIC-III trialJeanne Tie; Yuxuan Wang; Jonathan M. Loree; et al. 2025. Nature Medicinejournal articleCited in: Hematology-Oncology and Precision Medicine (passage 1); Hematology-Oncology and Precision Medicine (passage 2)
- Artificial intelligence agents in cancer research and oncologyDaniel Truhn; Shekoofeh Azizi; James Zou; et al. 2026. Nature Reviews Cancerjournal articleCited in: Hematology-Oncology and Precision Medicine
- Foundation Model for Predicting Prognosis and Adjuvant Therapy Benefit From Digital Pathology in GI CancersXiyue Wang; Yuming Jiang; Sen Yang; et al. 2025. Journal of Clinical Oncologyjournal articleCited in: Hematology-Oncology and Precision Medicine
- Evaluating the clinical utility of large language models for hepatocellular carcinoma treatment recommendations: A nationwide retrospective registry studyKeungmo Yang; Jaejun Lee; Jeong Won Jang; et al. 2026. PLOS Medicinejournal articleCited in: Hematology-Oncology and Precision Medicine
- Multicenter study on the versatility and adoption of AI-driven automated radiotherapy planning across cancer typesLei Yu; Qianxi Ni; Binbing Wang; et al. 2025. Nature Communicationsjournal articleCited in: Hematology-Oncology and Precision Medicine
- Machine Learning Predicts Acute Kidney Injury in Hospitalized Patients with Sickle Cell DiseaseRima S. Zahr; Akram Mohammed; Surabhi Naik; et al. 2024. American Journal of Nephrologyjournal articleCited in: Hematology-Oncology and Precision Medicine
- The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery and DevelopmentHarrison G. Zhang; Peter Eckmann; Jiacheng Miao; et al. 2026preprintCited in: Hematology-Oncology and Precision Medicine
Cardiology and Cardiothoracic Surgery
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- EchoJEPA-LCited in: Cardiology and Cardiothoracic Surgery
- FDA 510(k) databaseCited in: Cardiology and Cardiothoracic Surgery
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- Complete AI-Enabled Echocardiography Interpretation With Multitask Deep LearningGregory Holste; Evangelos K. Oikonomou; Márton Tokodi; et al. 2025. JAMAjournal articleCited in: Cardiology and Cardiothoracic Surgery
- Pragmatic Approaches to the Evaluation and Monitoring of Artificial Intelligence in Health Care: A Science Advisory From the American Heart AssociationSneha S. Jain; Shinichi Goto; Jennifer L. Hall; et al. 2025. Circulationjournal articleCited in: Cardiology and Cardiothoracic Surgery (passage 1); Cardiology and Cardiothoracic Surgery (passage 2)
- Jain et al., 2026Cited in: Cardiology and Cardiothoracic Surgery
- JAMA Cardiology editorial, November 2025Cited in: Cardiology and Cardiothoracic Surgery
- An Electrocardiogram Foundation Model Built on over 10 Million RecordingsJun Li; Aaron D. Aguirre; Valdery Moura Junior; et al. 2025. NEJM AIjournal articleCited in: Cardiology and Cardiothoracic Surgery
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- Video-based AI for beat-to-beat assessment of cardiac functionDavid Ouyang; Bryan He; Amirata Ghorbani; et al. 2020. Naturejournal articleCited in: Cardiology and Cardiothoracic Surgery
- Association of Coronary Artery Calcium Detected by Routine Ungated CT Imaging With Cardiovascular OutcomesAllison W. Peng; Ramzi Dudum; Sneha S. Jain; et al. 2023. Journal of the American College of Cardiologyjournal articleCited in: Cardiology and Cardiothoracic Surgery
- Large-Scale Assessment of a Smartwatch to Identify Atrial FibrillationMarco V. Perez; Kenneth W. Mahaffey; Haley Hedlin; et al. 2019. New England Journal of Medicinejournal articleCited in: Cardiology and Cardiothoracic Surgery (passage 1); Cardiology and Cardiothoracic Surgery (passage 2)
- Philips, March 2026Cited in: Cardiology and Cardiothoracic Surgery
- Ross & Swetlitz, 2018Cited in: Cardiology and Cardiothoracic Surgery
- Computer-Interpreted ElectrocardiogramsJürg Schläpfer; Hein J. Wellens. 2017. Journal of the American College of Cardiologyjournal articleCited in: Cardiology and Cardiothoracic Surgery
- Racial Bias in Pulse Oximetry MeasurementMichael W. Sjoding; Robert P. Dickson; Theodore J. Iwashyna; et al. 2020. New England Journal of Medicinejournal articleCited in: Cardiology and Cardiothoracic Surgery
- Somashekhar et al., 2018Cited in: Cardiology and Cardiothoracic Surgery
- End-to-end deep learning–based electrocardiographic analysis for the detection of hypertrophic cardiomyopathyJianqiang Song; Xiaoya Jiang; Yingying Zheng; et al. 2026. npj Digital Medicinejournal articleCited in: Cardiology and Cardiothoracic Surgery (passage 1); Cardiology and Cardiothoracic Surgery (passage 2)
- Spertus et al., 2025Cited in: Cardiology and Cardiothoracic Surgery (passage 1); Cardiology and Cardiothoracic Surgery (passage 2)
- Heart failure detection in electrocardiograms using artificial intelligence and pragmatic labellingElias Stenhede; Jesper Ravn; Henrik Schirmer; et al. 2026. npj Digital Medicinejournal articleCited in: Cardiology and Cardiothoracic Surgery
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-CardiologyEmma Svennberg; Janet K Han; Enrico G Caiani; et al. 2025. Europacejournal articleCited in: Cardiology and Cardiothoracic Surgery (passage 1); Cardiology and Cardiothoracic Surgery (passage 2); Cardiology and Cardiothoracic Surgery (passage 3)
- Tian et al., 2021Cited in: Cardiology and Cardiothoracic Surgery
- van Steijn et al., 2026Cited in: Cardiology and Cardiothoracic Surgery (passage 1); Cardiology and Cardiothoracic Surgery (passage 2)
- Comprehensive echocardiogram evaluation with view primed vision language AIMilos Vukadinovic; I-Min Chiu; Xiu Tang; et al. 2025. Naturejournal articleCited in: Cardiology and Cardiothoracic Surgery
- Performance of Emergency Department Screening Criteria for an Early ECG to Identify ST‐Segment Elevation Myocardial InfarctionMaame Yaa A. B. Yiadom; Christopher W. Baugh; Conor M. McWade; et al. 2017. Journal of the American Heart Associationjournal articleCited in: Cardiology and Cardiothoracic Surgery
- Phenomapping-derived tool to individualize the effect of sacubitril-valsartan in heart failure with preserved ejection fractionMinjae Yoon; Wonse Kim; Jin Joo Park; et al. 2026. npj Digital Medicinejournal articleCited in: Cardiology and Cardiothoracic Surgery
Recommended reading
- github.com/CarDS-Yale/PanEchoRecommended in: Cardiology and Cardiothoracic Surgery
- github.com/echonet/EchoPrimeRecommended in: Cardiology and Cardiothoracic Surgery
Neurology and Neurological Surgery
References
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- ClinicalTrials.gov, NCT05279755Cited in: Neurology and Neurological Surgery
- ClinicalTrials.gov, NCT06215755Cited in: Neurology and Neurological Surgery
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- DEN240062Cited in: Neurology and Neurological Surgery
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- Longitude Prize on ALSCited in: Neurology and Neurological Surgery
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- Plasma phospho-tau217 for Alzheimer’s disease diagnosis in primary and secondary care using a fully automated platformSebastian Palmqvist; Noëlle Warmenhoven; Federica Anastasi; et al. 2025. Nature Medicinejournal articleCited in: Neurology and Neurological Surgery
- ProJenX, September 2024Cited in: Neurology and Neurological Surgery
- Identification of drug repurposing candidates for amyotrophic lateral sclerosis using electronic health records: a retrospective cohort studyRichard J Reimer; Braden Soper; Jennifer L Wilson; et al. 2026. The Lancet Digital Healthjournal articleCited in: Neurology and Neurological Surgery (passage 1); Neurology and Neurological Surgery (passage 2)
- Sarhan et al., 2025Cited in: Neurology and Neurological Surgery
- Time Is Brain—QuantifiedJeffrey L. Saver. 2006. Strokejournal articleCited in: Neurology and Neurological Surgery
- Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligenceTomás Segura; Ignacio H. Medrano; Sergio Collazo; et al. 2023. Scientific Reportsjournal articleCited in: Neurology and Neurological Surgery
- Estimation of forced vital capacity using speech acoustics in patients with ALSGabriela M. Stegmann; Shira Hahn; Cayla J. Duncan; et al. 2021. Amyotrophic Lateral Sclerosis and Frontotemporal Degenerationjournal articleCited in: Neurology and Neurological Surgery
- Machine learning predicts treatment response to nusinersen in non-sitter Spinal Muscular Atrophy (SMA).Georgia Stimpson; Emer O'Reilly; Giorgia Coratti; et al. 2025preprintCited in: Neurology and Neurological Surgery
- Comparing artificial intelligence and physician performance in predicting IDH mutation status in gliomaSatoshi Takahashi; Masamichi Takahashi; Manabu Kinoshita; et al. 2026. npj Digital Medicinejournal articleCited in: Neurology and Neurological Surgery
- Spinal Muscular Atrophy Hypotonia Detection Using Computer Vision and Artificial IntelligenceAdel Taleb; Philippe Rambaud; Samuel Diop; et al. 2024. JAMA Pediatricsjournal articleCited in: Neurology and Neurological Surgery
- A multimodal sleep foundation model for disease predictionRahul Thapa; Magnus Ruud Kjaer; Bryan He; et al. 2026. Nature Medicinejournal articleCited in: Neurology and Neurological Surgery
- Developer perspectives on the ethics of AI-driven neural implants: a qualitative studyOdile C. van Stuijvenberg; Marike L. D. Broekman; Samantha E. C. Wolff; et al. 2024. Scientific Reportsjournal articleCited in: Neurology and Neurological Surgery
- The ethical significance of user-control in AI-driven speech-BCIs: a narrative reviewO. C. van Stuijvenberg; D. P. S. Samlal; M. J. Vansteensel; et al. 2024. Frontiers in Human Neurosciencejournal articleCited in: Neurology and Neurological Surgery
- Remote monitoring of amyotrophic lateral sclerosis using wearable sensors detects differences in disease progression and survival: a prospective cohort studyJordi W.J. van Unnik; Myrte Meyjes; Mark R. Janse van Mantgem; et al. 2024. eBioMedicinejournal articleCited in: Neurology and Neurological Surgery
- An instantaneous voice-synthesis neuroprosthesisMaitreyee Wairagkar; Nicholas S. Card; Tyler Singer-Clark; et al. 2025. Naturejournal articleCited in: Neurology and Neurological Surgery
- Predicting suicide attempts in adolescents with longitudinal clinical data and machine learningColin G. Walsh; Jessica D. Ribeiro; Joseph C. Franklin. 2018. Journal of Child Psychology and Psychiatryjournal articleCited in: Neurology and Neurological Surgery
- Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction modelHenk-Jan Westeneng; Thomas P A Debray; Anne E Visser; et al. 2018. The Lancet Neurologyjournal articleCited in: Neurology and Neurological Surgery
- Effect of a clinical decision support system on stroke care quality and outcomes in patients with acute ischaemic stroke (GOLDEN BRIDGE II): cluster randomised clinical trialXinmiao Zhang; Lingling Ding; Jing Jing; et al. 2026. BMJjournal articleCited in: Neurology and Neurological Surgery
- Gene expression signatures from whole blood predict amyotrophic lateral sclerosis case status and survivalYue Zhao; Masha G. Savelieff; Xiayan Li; et al. 2025. Nature Communicationsjournal articleCited in: Neurology and Neurological Surgery
Recommended reading
Psychiatry and Behavioral Health
References
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- Ajmani et al., 2025, preprintCited in: Psychiatry and Behavioral Health
- AMA Augmented Intelligence in MedicineCited in: Psychiatry and Behavioral Health
- Anthropic, 2025Cited in: Psychiatry and Behavioral Health
- APA Position Statement on the Role of Augmented Intelligence in Clinical Practice and Research (PDF)Cited in: Psychiatry and Behavioral Health
- Choi et al., 2024Cited in: Psychiatry and Behavioral Health
- A Decision-Support System to Personalize Antidepressant Treatment in Major Depressive DisorderAndrea Cipriani; Karen Barros Parron Fernandes; Benoit H. Mulsant; et al. 2026. JAMAjournal articleCited in: Psychiatry and Behavioral Health
- Colby et al., 1966Cited in: Psychiatry and Behavioral Health
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applicationsPablo Cruz-Gonzalez; Aaron Wan-Jia He; Elly PoPo Lam; et al. 2025. Psychological Medicinejournal articleCited in: Psychiatry and Behavioral Health
- DEN160018Cited in: Psychiatry and Behavioral Health
- A scoping review on the mental health harms of LLM-based chatbotsAlexander Diel; John Torous; Pim Cuijpers; et al. 2026. npj Digital Medicinejournal articleCited in: Psychiatry and Behavioral Health (passage 1); Psychiatry and Behavioral Health (passage 2); Psychiatry and Behavioral Health (passage 3)
- AI in mental healthSimon D’Alfonso. 2020. Current Opinion in Psychologyjournal articleCited in: Psychiatry and Behavioral Health
- Farzan et al., 2025Cited in: Psychiatry and Behavioral Health
- FDA DHAC, November 2025Cited in: Psychiatry and Behavioral Health
- Effectiveness of AI-Driven Conversational Agents in Improving Mental Health Among Young People: Systematic Review and Meta-AnalysisYi Feng; Yaming Hang; Wenzhi Wu; et al. 2025. Journal of Medical Internet Researchjournal articleCited in: Psychiatry and Behavioral Health
- Efficacy, User Engagement, and Acceptability of Cognitive Behavioral Therapy–Oriented Psychological Chatbots for Adults With Depressive and/or Anxiety Symptoms: Systematic Review and Meta-Analysis of Randomized Controlled TrialsBingyan Gong; Nisha Yao; Hangxin Xie; et al. 2026. Journal of Medical Internet Researchjournal articleCited in: Psychiatry and Behavioral Health
- The effectiveness of CBT-based NLP-enabled AI conversational agents for mental health intervention: a systematic review and meta-analysisYaming Hang; Wenzhi Wu; Yi Feng; et al. 2026. npj Digital Medicinejournal articleCited in: Psychiatry and Behavioral Health
- Charting the evolution of artificial intelligence mental health chatbots from rule‐based systems to large language models: a systematic reviewYining Hua; Steve Siddals; Zilin Ma; et al. 2025. World Psychiatryjournal articleCited in: Psychiatry and Behavioral Health
- Design and feasibility of smartphone-based digital phenotyping for long-term mental health monitoring in adolescentsDebbie Huang; Patrick Emedom-Nnamdi; Jukka-Pekka Onnela; et al. 2025. PLOS Digital Healthjournal articleCited in: Psychiatry and Behavioral Health
- The Use of Artificial Intelligence for Personalized Treatment in PsychiatrySara Jalali; Qiong You; Victoria Xu; et al. 2025. Current Psychiatry Reportsjournal articleCited in: Psychiatry and Behavioral Health
- K191716Cited in: Psychiatry and Behavioral Health
- K231209Cited in: Psychiatry and Behavioral Health
- Suicide and self-harmDuleeka Knipe; Prianka Padmanathan; Giles Newton-Howes; et al. 2022. The Lancetjournal articleCited in: Psychiatry and Behavioral Health
- Current evidence on the efficacy of mental health smartphone apps for symptoms of depression and anxiety. A meta‐analysis of 176 randomized controlled trialsJake Linardon; John Torous; Joseph Firth; et al. 2024. World Psychiatryjournal articleCited in: Psychiatry and Behavioral Health
- Use of Generative AI for Mental Health Advice Among US Adolescents and Young AdultsRyan K. McBain; Robert Bozick; Melissa Diliberti; et al. 2025. JAMA Network Openjournal articleCited in: Psychiatry and Behavioral Health
- Meta Engineering, 2018Cited in: Psychiatry and Behavioral Health
- Meta, 2026Cited in: Psychiatry and Behavioral Health (passage 1); Psychiatry and Behavioral Health (passage 2)
- Moore et al., 2025, preprintCited in: Psychiatry and Behavioral Health
- NBC News, October 2024Cited in: Psychiatry and Behavioral Health
- Nguyen et al., JAACAP, 2024Cited in: Psychiatry and Behavioral Health
- NICE NG225Cited in: Psychiatry and Behavioral Health
- Potentially harmful consequences of artificial intelligence (AI) chatbot use among patients with mental illness: Early data from a large psychiatric service systemSidse Godske Olsen; Christian Jon Reinecke-Tellefsen; Søren Dinesen Østergaard. 2025preprintCited in: Psychiatry and Behavioral Health (passage 1); Psychiatry and Behavioral Health (passage 2)
- OpenAI, 2025Cited in: Psychiatry and Behavioral Health (passage 1); Psychiatry and Behavioral Health (passage 2)
- Performance of mental health chatbot agents in detecting and managing suicidal ideationW. Pichowicz; M. Kotas; P. Piotrowski. 2025. Scientific Reportsjournal articleCited in: Psychiatry and Behavioral Health
- Pierre et al., 2025Cited in: Psychiatry and Behavioral Health
- Evaluation of Large Language Model Chatbot Responses to Psychotic PromptsElaine Shen; Fadi Hamati; Meghan Rose Donohue; et al. 2026. JAMA Psychiatryjournal articleCited in: Psychiatry and Behavioral Health (passage 1); Psychiatry and Behavioral Health (passage 2)
- Real-world use of large language models for mental health in 2024Elizabeth C. Stade; Zoe M. Tait; Samuel T. Campione; et al. 2026. npj Digital Medicinejournal articleCited in: Psychiatry and Behavioral Health
- The evolving field of digital mental health: current evidence and implementation issues for smartphone apps, generative artificial intelligence, and virtual realityJohn Torous; Jake Linardon; Simon B. Goldberg; et al. 2025. World Psychiatryjournal articleCited in: Psychiatry and Behavioral Health
- Utah Code, Title 13, Chapter 72aCited in: Psychiatry and Behavioral Health
- VA/DoD Clinical Practice Guideline for Assessment and Management of Patients at Risk for Suicide (2024)Cited in: Psychiatry and Behavioral Health
- Prospective Validation of an Electronic Health Record–Based, Real-Time Suicide Risk ModelColin G. Walsh; Kevin B. Johnson; Michael Ripperger; et al. 2021. JAMA Network Openjournal articleCited in: Psychiatry and Behavioral Health
- Evaluating transportability failures of electronic health record-based risk models for treatment-resistant depressionColin G. Walsh; Michael Ripperger; Thomas H. McCoy Jr; et al. 2026. npj Digital Medicinejournal articleCited in: Psychiatry and Behavioral Health
- A clinically validated framework for auditing AI chatbot behavior in mental health interactionsVeith Weilnhammer; Kevin YC Hou; Lennart Luettgau; et al. 2026. Nature Medicinejournal articleCited in: Psychiatry and Behavioral Health
- Wellness and Oversight for Psychological Resources Act, Public Act 104-0054Cited in: Psychiatry and Behavioral Health
- White House, 2025Cited in: Psychiatry and Behavioral Health
- Integration of Face-to-Face Screening With Real-time Machine Learning to Predict Risk of Suicide Among AdultsDrew Wilimitis; Robert W. Turer; Michael Ripperger; et al. 2022. JAMA Network Openjournal articleCited in: Psychiatry and Behavioral Health
Primary Care, Family Medicine, and Preventive Medicine
References
- AAFP survey report, 2025Cited in: Primary Care, Family Medicine, and Preventive Medicine
- aafp.org/artificial-intelligenceCited in: Primary Care, Family Medicine, and Preventive Medicine
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine (passage 1); Primary Care, Family Medicine, and Preventive Medicine (passage 2)
- A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-BeingMajid Afshar; Mary Ryan Baumann; Felice Resnik; et al. 2025. NEJM AIjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trialAmbrose Agweyu; Paul Mwaniki; Vaishnavi Menon; et al. 2026. Nature Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Safety of a large language model-based clinical decision support system in African primary healthcareAmbrose Agweyu; Paul Mwaniki; Wilkister Musau; et al. 2026. Nature Healthjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Artificial Intelligence and Machine Learning for Primary Care curriculumCited in: Primary Care, Family Medicine, and Preventive Medicine
- Reinforcement Learning to Prevent Acute Care Events Among Medicaid Populations: Mixed Methods StudySanjay Basu; Bhairavi Muralidharan; Parth Sheth; et al. 2025. JMIR AIjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a RealityDavid W. Bates; Gilad J. Kuperman; Samuel Wang; et al. 2003. Journal of the American Medical Informatics Associationjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Ethical Application of Artificial Intelligence in Family Medicine policyCited in: Primary Care, Family Medicine, and Preventive Medicine
- FDA 510(k) summaryCited in: Primary Care, Family Medicine, and Preventive Medicine
- FDA DEN180001Cited in: Primary Care, Family Medicine, and Preventive Medicine (passage 1); Primary Care, Family Medicine, and Preventive Medicine (passage 2)
- Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled TrialKathleen Kara Fitzpatrick; Alison Darcy; Molly Vierhile. 2017. JMIR Mental Healthjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Health IT End-Users Alliance, 2026Cited in: Primary Care, Family Medicine, and Preventive Medicine
- Kansagara et al., 2011Cited in: Primary Care, Family Medicine, and Preventive Medicine
- Patterns of online consultation use in Great Britain, 2019–2023: an observational analysisGabriele Kerr; Geva Greenfield; Alex Bottle; et al. 2025. BMJ Digital Health & AIjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Promoting effective transitions of care at hospital discharge: A review of key issues for hospitalistsSunil Kripalani; Amy T. Jackson; Jeffrey L. Schnipper; et al. 2007. Journal of Hospital Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Ambient AI Scribes in Clinical Practice: A Randomized TrialPaul J. Lukac; William Turner; Sitaram Vangala; et al. 2025. NEJM AIjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- MobiHealthNews, 2026Cited in: Primary Care, Family Medicine, and Preventive Medicine
- Evidence and Recommendations on the Use of Telemedicine for the Management of Arterial HypertensionStefano Omboni; Richard J. McManus; Hayden B. Bosworth; et al. 2020. Hypertensionjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Large-Scale Assessment of a Smartwatch to Identify Atrial FibrillationMarco V. Perez; Kenneth W. Mahaffey; Haley Hedlin; et al. 2019. New England Journal of Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Prediction of cardiovascular risk factors from retinal fundus photographs via deep learningRyan Poplin; Avinash V. Varadarajan; Katy Blumer; et al. 2018. Nature Biomedical Engineeringjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Large Language Model Performance and Clinical Reasoning TasksArya S. Rao; Kaiz P. Esmail; Richard S. Lee; et al. 2026. JAMA Network Openjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine (passage 1); Primary Care, Family Medicine, and Preventive Medicine (passage 2)
- Evaluation of symptom checkers for self diagnosis and triage: audit studyHannah L Semigran; Jeffrey A Linder; Courtney Gidengil; et al. 2015. BMJjournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine (passage 1); Primary Care, Family Medicine, and Preventive Medicine (passage 2)
- Patient and Physician Reminders to Promote Colorectal Cancer ScreeningThomas D. Sequist; Alan M. Zaslavsky; Richard Marshall; et al. 2009. Archives of Internal Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Large language models encode clinical knowledgeKaran Singhal; Shekoofeh Azizi; Tao Tu; et al. 2023. Naturejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- An LLM chatbot to facilitate primary-to-specialist care transitions: a randomized controlled trialXinge Tao; Shuya Zhou; Kai Ding; et al. 2026. Nature Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- Towards conversational diagnostic artificial intelligenceTao Tu; Mike Schaekermann; Anil Palepu; et al. 2025. Naturejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- An online randomized trial of electronic nudges for influenza vaccination willingness among older Chinese adultsFan Yang; Ban Hu; Ning Huang; et al. 2026. npj Digital Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine (passage 1); Primary Care, Family Medicine, and Preventive Medicine (passage 2)
- Comparison of Initial Artificial Intelligence (AI) and Final Physician Recommendations in AI-Assisted Virtual Urgent Care VisitsDan Zeltzer; Zehavi Kugler; Lior Hayat; et al. 2025. Annals of Internal Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- PRIMARY-AI: outcomes-based standards to safeguard primary care in the AI eraDian Zeng; Lorainne Tudor Car; Kamlesh Khunti; et al. 2026. Nature Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine
- A Multimodal large language model-based triage tool for osteoporotic vertebral compression fractures using posture and movement videosMeiwei Zhang; Xiaoqing Jin; Shicai Xu; et al. 2026. npj Digital Medicinejournal articleCited in: Primary Care, Family Medicine, and Preventive Medicine (passage 1); Primary Care, Family Medicine, and Preventive Medicine (passage 2)
Pathology and Laboratory Medicine
References
- AIM-HER2 Breast CancerCited in: Pathology and Laboratory Medicine
- Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational studyHeling Bao; Hui Bi; Xiaosong Zhang; et al. 2020. Gynecologic Oncologyjournal articleCited in: Pathology and Laboratory Medicine
- Real-world deployment of a fine-tuned pathology foundation model for lung cancer biomarker detectionGabriele Campanella; Neeraj Kumar; Swaraj Nanda; et al. 2025. Nature Medicinejournal articleCited in: Pathology and Laboratory Medicine
- CAP and ASCO HER2 Testing Guideline UpdateCited in: Pathology and Laboratory Medicine
- A multi-class gastric biopsy artificial intelligence model developed from whole slide histopathological imagesJian-Ning Chen; Chang Zhao; Hui Chen; et al. 2026. npj Digital Medicinejournal articleCited in: Pathology and Laboratory Medicine (passage 1); Pathology and Laboratory Medicine (passage 2)
- AI-based histopathology image analysis reveals a distinct subset of endometrial cancersAmirali Darbandsari; Hossein Farahani; Maryam Asadi; et al. 2024. Nature Communicationsjournal articleCited in: Pathology and Laboratory Medicine
- FDA Breakthrough Devices ProgramCited in: Pathology and Laboratory Medicine
- FDA K241232Cited in: Pathology and Laboratory Medicine
- FDA K243391Cited in: Pathology and Laboratory Medicine
- FDA, DEN200080Cited in: Pathology and Laboratory Medicine
- Prospective Clinical Implementation of Paige Prostate Detect Artificial Intelligence Assistance in the Detection of Prostate Cancer in Prostate Biopsies: CONFIDENT P Trial Implementation of Artificial Intelligence Assistance in Prostate Cancer DetectionRachel N. Flach; Carmen van Dooijeweert; Tri Q. Nguyen; et al. 2025. JCO Clinical Cancer Informaticsjournal articleCited in: Pathology and Laboratory Medicine (passage 1); Pathology and Laboratory Medicine (passage 2)
- Validation of a digital pathology system including remote review during the COVID-19 pandemicMatthew G. Hanna; Victor E. Reuter; Orly Ardon; et al. 2020. Modern Pathologyjournal articleCited in: Pathology and Laboratory Medicine
- Interpretive Diagnostic Error Reduction guidelineCited in: Pathology and Laboratory Medicine
- An Overview of Artificial Intelligence in Gynaecological Pathology DiagnosticsAnna Joshua; Katie E. Allen; Nicolas M. Orsi. 2025. Cancersjournal articleCited in: Pathology and Laboratory Medicine
- Spatial biomarker discovery via interpretable semantic learning in histopathologyJunhao Liang; Xiaofeng Jiang; Nic Gabriel Reitsam; et al. 2026. Cancer Celljournal articleCited in: Pathology and Laboratory Medicine
- Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathologyElisa Lin; Franklin Fuda; Hung S Luu; et al. 2023. Seminars in Diagnostic Pathologyjournal articleCited in: Pathology and Laboratory Medicine (passage 1); Pathology and Laboratory Medicine (passage 2)
- Liu et al., eClinicalMedicine, 2024Cited in: Pathology and Laboratory Medicine (passage 1); Pathology and Laboratory Medicine (passage 2)
- nnMIL: a generalizable multiple instance learning framework for computational pathologyXiangde Luo; Jinxi Xiang; Yuanfeng Ji; et al. 2026. Nature Biomedical Engineeringjournal articleCited in: Pathology and Laboratory Medicine (passage 1); Pathology and Laboratory Medicine (passage 2)
- NCI Digital Pathology Workshop, J Pathol Inform, 2025Cited in: Pathology and Laboratory Medicine
- An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment studyLiron Pantanowitz; Gabriela M Quiroga-Garza; Lilach Bien; et al. 2020. The Lancet Digital Healthjournal articleCited in: Pathology and Laboratory Medicine
- Beyond expertise: exploring behavioral personas in AI-assisted rare renal cancer diagnosisChanghyun Park; Yong Il Lee; Jinew Seo; et al. 2026. npj Digital Medicinejournal articleCited in: Pathology and Laboratory Medicine (passage 1); Pathology and Laboratory Medicine (passage 2)
- PathAI announcementCited in: Pathology and Laboratory Medicine
- PathAI, 2023Cited in: Pathology and Laboratory Medicine
- Detecting changes in the performance of a clinical machine learning tool over timeMichiel Schinkel; Anneroos W. Boerman; Ketan Paranjape; et al. 2023. eBioMedicinejournal articleCited in: Pathology and Laboratory Medicine
- Artificial Intelligence–Assisted Colonoscopy for Polyp DetectionSaeed Soleymanjahi; Jack Huebner; Lina Elmansy; et al. 2024. Annals of Internal Medicinejournal articleCited in: Pathology and Laboratory Medicine (passage 1); Pathology and Laboratory Medicine (passage 2)
- Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast CancerDavid F. Steiner; Robert MacDonald; Yun Liu; et al. 2018. American Journal of Surgical Pathologyjournal articleCited in: Pathology and Laboratory Medicine (passage 1); Pathology and Laboratory Medicine (passage 2)
- whole-slide imaging validation guidelineCited in: Pathology and Laboratory Medicine
- Xu et al., 2025Cited in: Pathology and Laboratory Medicine
- Tracking cancer lesions on surgical samples of gastric cancer by artificial intelligent algorithmsRuixin Yang; Chao Yan; Sheng Lu; et al. 2021. Journal of Cancerjournal articleCited in: Pathology and Laboratory Medicine
- Prediction of prognosis and treatment response in ovarian cancer patients from histopathology images using graph deep learning: a multicenter retrospective studyZijian Yang; Yibo Zhang; Lili Zhuo; et al. 2024. European Journal of Cancerjournal articleCited in: Pathology and Laboratory Medicine
Dermatology
References
- Limits of Artificial Intelligence Models for Skin Cancer Diagnosis in Realistic SettingsJulien Anriot; Siyuan Yan; Clio Coste; et al. 2026. JAMA Dermatologyjournal articleCited in: Dermatology
- Cai et al., 2025Cited in: Dermatology
- FDA, DEN230008Cited in: Dermatology
- FDA, P150046Cited in: Dermatology
- DERM-SUCCESS FDA Pivotal Study: A Multi-Reader Multi-Case Evaluation of Primary Care Physicians’ Skin Cancer Detection Using AI-Enabled Elastic Scattering SpectroscopyLaura K. Ferris; Erik Jaklitsch; Elizabeth V. Seiverling; et al. 2025. Journal of Primary Care & Community Healthjournal articleCited in: Dermatology
- Joerg et al., 2025Cited in: Dermatology
- Prospective Evidence on Artificial Intelligence−Assisted Melanoma DiagnosticsSara Laiouar-Pedari; Arlene Kühn; Christoph Wies; et al. 2026. JAMA Dermatologyjournal articleCited in: Dermatology
- Diagnostic accuracy of teledermatology for skin diseases: a systematic review and meta-analysisKatalin Martyin; Fanni Adél Meznerics; Laura Anna Bokor; et al. 2026. Frontiers in Medicinejournal articleCited in: Dermatology
- Cost-effectiveness of an AI-based app compared to usual care for early skin cancer detection in BelgiumAnnick Meertens; Lieve Brochez; Emma Coussens; et al. 2026. npj Digital Medicinejournal articleCited in: Dermatology (passage 1); Dermatology (passage 2); Dermatology (passage 3)
- Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysisNorhane Nadour; Théo Duguet; Sophie Zahedi; et al. 2025. BMC Primary Carejournal articleCited in: Dermatology
- AI ‐Based Objective Severity Assessment of Atopic Dermatitis Using Patient Photos in a Real‐World Setting: A Digital Biomarker ApproachUtako Okata‐Karigane; Masakazu Hirota; Chiaki Takahashi; et al. 2025. Allergyjournal articleCited in: Dermatology
- Consumer Understanding of Skin Concerns With an AI-Powered Informational ToolRory Sayres; Ayush Jain; Maya Venkatraman; et al. 2026. JAMA Dermatologyjournal articleCited in: Dermatology
- Evaluating skin tone scales for dermatologic dataset labeling: a prospective-comparative studyVanessa R. Weir; Yingjoy Li; Maura C. Gillis; et al. 2025. npj Digital Medicinejournal articleCited in: Dermatology
- Divergent impacts of explainable AI for dermatological diagnosis on clinicians versus lay peopleXuhai ‘Orson’ Xu; Haoyu Hu; Haoran Zhang; et al. 2026. Nature Medicinejournal articleCited in: Dermatology
Ophthalmology
References
- 2024 updateCited in: Ophthalmology
- AAO Diabetic Retinopathy PPPCited in: Ophthalmology (passage 1); Ophthalmology (passage 2)
- Five-year cost-effectiveness of AI for adult diabetic eye exams—a health system perspectiveMahnoor Ahmed; Michael D. Abramoff; Harold P. Lehmann; et al. 2026. npj Digital Medicinejournal articleCited in: Ophthalmology (passage 1); Ophthalmology (passage 2); Ophthalmology (passage 3); Ophthalmology (passage 4)
- AREDS2-HOME Study Research Group, 2014Cited in: Ophthalmology
- Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural NetworksJames M. Brown; J. Peter Campbell; Andrew Beers; et al. 2018. JAMA Ophthalmologyjournal articleCited in: Ophthalmology
- Clinical setting-dependent diagnostic accuracy of artificial intelligence and store-and-forward diabetic retinopathy screening: a systematic review and meta-analysisKai-Yang Chen; Hoi-Chun Chan; Chi-Ming Chan. 2026. npj Digital Medicinejournal articleCited in: Ophthalmology (passage 1); Ophthalmology (passage 2)
- AIROGS: Artificial Intelligence for Robust Glaucoma Screening ChallengeCoen de Vente; Koenraad A. Vermeer; Nicolas Jaccard; et al. 2024. IEEE Transactions on Medical Imagingjournal articleCited in: Ophthalmology
- FDA 510(k) summaryCited in: Ophthalmology
- FDA DEN180001Cited in: Ophthalmology (passage 1); Ophthalmology (passage 2); Ophthalmology (passage 3)
- FDA K091579Cited in: Ophthalmology
- FDA K240058Cited in: Ophthalmology
- Toward Multimodal Conversational AI for Age-Related Macular DegenerationRan Gu. 2026. Ophthalmology Retinajournal articleCited in: Ophthalmology
- AI-based clinician decision support system for diagnosis of inherited retinal diseases: a multicenter, randomized trialHuixun Jia; Bo Qian; Yanlin Qu; et al. 2026. Nature Medicinejournal articleCited in: Ophthalmology
- Autonomous AI-assisted diabetic retinopathy screening at primary care is associated with increased presentation to eye care by at risk patientsAriel Leong; Risa M. Wolf; Roomasa Channa; et al. 2026. npj Digital Medicinejournal articleCited in: Ophthalmology (passage 1); Ophthalmology (passage 2)
- Generative artificial intelligence for fundus fluorescein angiography interpretation and human expert evaluationAn Shao; Xiaocong Liu; Wenyue Shen; et al. 2025. npj Digital Medicinejournal articleCited in: Ophthalmology
- Teng et al., 2025Cited in: Ophthalmology
- Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trialRisa M. Wolf; Roomasa Channa; T. Y. Alvin Liu; et al. 2024. Nature Communicationsjournal articleCited in: Ophthalmology
- Initial lessons from real-world implementation of an AI-agent eye clinic in ChinaTao Yan; Di Zhang; Luxiao Chen; et al. 2026. Nature Medicinejournal articleCited in: Ophthalmology
- Zhang et al., 2026Cited in: Ophthalmology (passage 1); Ophthalmology (passage 2)
Orthopedic Surgery and Physical Medicine
References
- AAOS Position Statement 1193Cited in: Orthopedic Surgery and Physical Medicine (passage 1); Orthopedic Surgery and Physical Medicine (passage 2)Recommended in: Orthopedic Surgery and Physical Medicine (passage 3)
- Al-Ghufaily et al., 2026Cited in: Orthopedic Surgery and Physical Medicine
- A clinical decision support tool for accurate hip fracture prediction: A nationwide cohort studyKristian F. Axelsson; Henrik Litsne; Konstantinos Konstantinou; et al. 2026. PLOS Medicinejournal articleCited in: Orthopedic Surgery and Physical Medicine
- FDA DEN180005Cited in: Orthopedic Surgery and Physical Medicine (passage 1); Orthopedic Surgery and Physical Medicine (passage 2); Orthopedic Surgery and Physical Medicine (passage 3); Orthopedic Surgery and Physical Medicine (passage 4)Recommended in: Orthopedic Surgery and Physical Medicine (passage 5)
- FDA K143690Cited in: Orthopedic Surgery and Physical Medicine
- ChatGPT’s role in alleviating anxiety in total knee arthroplasty consent process: a randomized controlled trial pilot studyWenyi Gan; Jianfeng Ouyang; Guorong She; et al. 2025. International Journal of Surgeryjournal articleCited in: Orthopedic Surgery and Physical Medicine
- An Artificial Intelligence–Based Clinical Decision Support Tool to Reduce Hyponatremia after Total Joint ArthroplastyKyle N. Kunze; James D. Beckman; Linda A. Russell; et al. 2026. NEJM Catalystjournal articleCited in: Orthopedic Surgery and Physical Medicine
- FDA-Cleared Artificial Intelligence Medical Devices in Orthopaedic SurgeryBranden Lee; Mitchell Jay; Henry Fox; et al. 2026. JAAOS: Global Research and Reviewsjournal articleCited in: Orthopedic Surgery and Physical Medicine
- A randomized controlled trial of a WeChat-based artificial intelligence agent for postoperative care in orthopedic patientsJuntan Li; Yuqi Zhang; Zihao Zhang; et al. 2026. npj Digital Medicinejournal articleCited in: Orthopedic Surgery and Physical Medicine
- Comparative efficacy of robotic exoskeleton and conventional gait training in patients with spinal cord injury: a meta-analysis of randomized controlled trialsShengye Liu; Fangyuan Chen; Jianqiao Yin; et al. 2025. Journal of NeuroEngineering and Rehabilitationjournal articleCited in: Orthopedic Surgery and Physical Medicine (passage 1)Recommended in: Orthopedic Surgery and Physical Medicine (passage 2)
- Maquer et al., 2026Cited in: Orthopedic Surgery and Physical Medicine
- Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after strokeJan Mehrholz; Marcus Pohl; Thomas Platz; et al. 2018. Cochrane Database of Systematic Reviewsjournal articleCited in: Orthopedic Surgery and Physical Medicine (passage 1)Recommended in: Orthopedic Surgery and Physical Medicine (passage 2)
- Electromechanical-assisted training for walking after strokeJan Mehrholz; Joachim Kugler; Marcus Pohl; et al. 2025. Cochrane Database of Systematic Reviewsjournal articleCited in: Orthopedic Surgery and Physical Medicine (passage 1)Recommended in: Orthopedic Surgery and Physical Medicine (passage 2)
- Clinical effectiveness and safety of powered exoskeleton-assisted walking in patients with spinal cord injury: systematic review with meta-analysisLarry Miller; Angela Zimmermann; William Herbert. 2016. Medical Devices: Evidence and Researchjournal articleCited in: Orthopedic Surgery and Physical Medicine (passage 1)Recommended in: Orthopedic Surgery and Physical Medicine (passage 2)
- A Prospective Approach to Integration of AI Fracture Detection Software in Radiographs into Clinical WorkflowJonas Oppenheimer; Sophia Lüken; Bernd Hamm; et al. 2023. Lifejournal articleCited in: Orthopedic Surgery and Physical Medicine (passage 1)Recommended in: Orthopedic Surgery and Physical Medicine (passage 2)
- Palmer, STAT News, March 2026Cited in: Orthopedic Surgery and Physical Medicine
- An algorithmic approach to reducing unexplained pain disparities in underserved populationsEmma Pierson; David M. Cutler; Jure Leskovec; et al. 2021. Nature Medicinejournal articleCited in: Orthopedic Surgery and Physical Medicine
- RecovryAI press release, March 2026Cited in: Orthopedic Surgery and Physical Medicine
- Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trialHelen Rodgers; Helen Bosomworth; Hermano I Krebs; et al. 2019. The Lancetjournal articleCited in: Orthopedic Surgery and Physical Medicine (passage 1)Recommended in: Orthopedic Surgery and Physical Medicine (passage 2)
- Two-dimensional video-based analysis of human gait using pose estimationJan Stenum; Cristina Rossi; Ryan T. Roemmich. 2021. PLOS Computational Biologyjournal articleCited in: Orthopedic Surgery and Physical Medicine (passage 1)Recommended in: Orthopedic Surgery and Physical Medicine (passage 2)
Recommended reading
- https://doi.org/10.1302/0301-620X.100B7.BJJ-2017-1449.R1Recommended in: Orthopedic Surgery and Physical Medicine
- Assessment of a deep-learning system for fracture detection in musculoskeletal radiographsRebecca M. Jones; Anuj Sharma; Robert Hotchkiss; et al. 2020. npj Digital Medicinejournal articleRecommended in: Orthopedic Surgery and Physical Medicine
- Deep neural network improves fracture detection by cliniciansRobert Lindsey; Aaron Daluiski; Sumit Chopra; et al. 2018. Proceedings of the National Academy of Sciencesjournal articleRecommended in: Orthopedic Surgery and Physical Medicine
- Patient Satisfaction Outcomes after Robotic Arm-Assisted Total Knee Arthroplasty: A Short-Term EvaluationRobert Marchand; Nipun Sodhi; Anton Khlopas; et al. 2017. The Journal of Knee Surgeryjournal articleRecommended in: Orthopedic Surgery and Physical Medicine
- www.aapmr.orgRecommended in: Orthopedic Surgery and Physical Medicine
- www.apta.orgRecommended in: Orthopedic Surgery and Physical Medicine
Infectious Diseases and Antimicrobial Stewardship
References
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction in Klebsiella pneumoniae: A Systematic Review of Clinical Microbiology ApplicationsRaghav Aggarwal; Nisarg Shah; Jolie Jin En Wong; et al. 2026. Infection and Drug Resistancejournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- A new version of Antibiogo is now available!Antibiogo. 2025. Official Antibiogo product updateCited in: Infectious Diseases and Antimicrobial Stewardship
- AntibiogoAntibiogo. 2026. Official Antibiogo product siteCited in: Infectious Diseases and Antimicrobial Stewardship
- Implementing an Antibiotic Stewardship Program: Guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of AmericaTamar F. Barlam; Sara E. Cosgrove; Lilian M. Abbo; et al. 2016. Clinical Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- The impact of artificial intelligence-driven decision support on uncertain antimicrobial prescribing: a randomised, multimethod studyWilliam J Bolton. 2025. The Lancet Digital Healthjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- CDC Core Elements of Hospital Antibiotic Stewardship ProgramsCited in: Infectious Diseases and Antimicrobial Stewardship (passage 1); Infectious Diseases and Antimicrobial Stewardship (passage 2)
- A deep learning system for bacterial identification and resistance prediction from MALDI-TOF dataChih-Hung Wang. 2026. npj Digital MedicineCited in: Infectious Diseases and Antimicrobial Stewardship
- Personalized antibiograms for machine learning driven antibiotic selectionConor K. Corbin; Lillian Sung; Arhana Chattopadhyay; et al. 2022. Communications Medicinejournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Direct carbapenemase typing from disc diffusion antibiograms with MALCA (MAchine Learning CArbapenemase)Cécile Emeraud. 2026. Nature Communicationsjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Nextstrain: real-time tracking of pathogen evolutionJames Hadfield; Colin Megill; Sidney M Bell; et al. 2018. Bioinformaticsjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Artificial intelligence-driven approaches in antibiotic stewardship programs and optimizing prescription practices: A systematic reviewHamid Harandi; Maryam Shafaati; Mohammadreza Salehi; et al. 2025. Artificial Intelligence in Medicinejournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- P-1295. Use of Large Language Models Does Not Improve Model Performance for Antimicrobial Resistance Prediction in Community- and Hospital-Onset Gram Negative SepsisAlison M Hixon; Hanyang Liu; Michael J Durkin; et al. 2026. Open Forum Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Artificial intelligence and infectious disease diagnostics: state of the art and future perspectivesLuca Miglietta; Timothy M Rawson; Ronald Galiwango; et al. 2026. The Lancet Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship (passage 1); Infectious Diseases and Antimicrobial Stewardship (passage 2)
- Global burden of bacterial antimicrobial resistance in 2019: a systematic analysisChristopher J L Murray; Kevin Shunji Ikuta; Fablina Sharara; et al. 2022. The Lancetjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Artificial intelligence and infectious diseases: an evidence-driven conceptual framework for research, public health, and clinical practiceAnna Odone; Chiara Barbati; Silvia Amadasi; et al. 2026. The Lancet Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- AI-based mobile application to fight antibiotic resistanceMarco Pascucci. 2021. Nature Communicationsjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracyFlavia Pennisi; Antonio Pinto; Giovanni Emanuele Ricciardi; et al. 2025. European Journal of Clinical Microbiology & Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Syndromic Panel-Based Testing in Clinical MicrobiologyPoornima Ramanan; Alexandra L. Bryson; Matthew J. Binnicker; et al. 2018. Clinical Microbiology Reviewsjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Ongoing Revolution in Bacteriology: Routine Identification of Bacteria by Matrix‐Assisted Laser Desorption Ionization Time‐of‐Flight Mass SpectrometryPiseth Seng; Michel Drancourt; Frédérique Gouriet; et al. 2009. Clinical Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- AI driven decision support reduces antibiotic mismatches and inappropriate use in outpatient urinary tract infectionsShirley Shapiro Ben David. 2025. npj Digital Medicinejournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- SHEA, 2024Cited in: Infectious Diseases and Antimicrobial Stewardship
- Are We Heeding the Warning Signs? Examining Providers’ Overrides of Computerized Drug-Drug Interaction Alerts in Primary CareSarah P. Slight; Diane L. Seger; Karen C. Nanji; et al. 2013. PLoS ONEjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- AntibiogoThe MSF Foundation. 2026. The MSF Foundation project pageCited in: Infectious Diseases and Antimicrobial Stewardship
- The Effect of Molecular Rapid Diagnostic Testing on Clinical Outcomes in Bloodstream Infections: A Systematic Review and Meta-analysisTristan T. Timbrook; Jacob B. Morton; Kevin W. McConeghy; et al. 2017. Clinical Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Overriding of Drug Safety Alerts in Computerized Physician Order EntryH. van der Sijs; J. Aarts; A. Vulto; et al. 2006. Journal of the American Medical Informatics Associationjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Lightweight non-contrast CT-based multimodal artificial intelligence for subtype classification of hepatic cystic echinococcosis in resource-limited clinical settingsZhanjin Wang; Xuxia A; Weiwei Xue; et al. 2026. npj Digital Medicinejournal articleCited in: Infectious Diseases and Antimicrobial Stewardship (passage 1); Infectious Diseases and Antimicrobial Stewardship (passage 2)
- WHO AWaRe antibiotic bookCited in: Infectious Diseases and Antimicrobial Stewardship
- WHO Global Action Plan on Antimicrobial Resistance, 2026–2036Cited in: Infectious Diseases and Antimicrobial Stewardship
- Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation studyNiklas Wiesmann. 2025. Journal of Clinical Microbiologyjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Correction for Wiesmann et al., “Prediction of antimicrobial resistance from MALDI-TOF mass spectra using machine learning: a validation study”Niklas Wiesmann. 2026. Journal of Clinical Microbiologyjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Wilkinson et al., 2021Cited in: Infectious Diseases and Antimicrobial Stewardship
- Data Requirements for Electronic Surveillance of Healthcare-Associated InfectionsKeith F. Woeltje; Michael Y. Lin; Michael Klompas; et al. 2014. Infection Control & Hospital Epidemiologyjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
- Wong et al., 2021Cited in: Infectious Diseases and Antimicrobial Stewardship
- Infectious Diseases Society of America Position Statement on Telehealth and Telemedicine as Applied to the Practice of Infectious DiseasesJeremy D Young; Rima Abdel-Massih; Thomas Herchline; et al. 2019. Clinical Infectious Diseasesjournal articleCited in: Infectious Diseases and Antimicrobial Stewardship
Allergy, Immunology, and Medical Genetics
References
- 2024 position statementCited in: Allergy, Immunology, and Medical Genetics
- 2024 practice guidelinesCited in: Allergy, Immunology, and Medical Genetics
- A rapid literature review of the impact of penicillin allergy on antibiotic resistanceShadia Ahmed; Jonathan A T Sandoe. 2025. JAC-Antimicrobial Resistancejournal articleCited in: Allergy, Immunology, and Medical Genetics
- Application of the Face2Gene tool in an Italian dysmorphological pediatric clinic: Retrospective validation and future perspectivesAlessia Carrer; Maria Giovanna Romaniello; Maria Letizia Calderara; et al. 2024. American Journal of Medical Genetics Part Ajournal articleCited in: Allergy, Immunology, and Medical Genetics
- CPIC guideline directoryCited in: Allergy, Immunology, and Medical Genetics (passage 1); Allergy, Immunology, and Medical Genetics (passage 2)
- Identifying facial phenotypes of genetic disorders using deep learningYaron Gurovich; Yair Hanani; Omri Bar; et al. 2019. Nature Medicinejournal articleCited in: Allergy, Immunology, and Medical Genetics
- A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research—A Work Group Report of the AAAAI Health Informatics, Technology, and Education CommitteePaneez Khoury; Renganathan Srinivasan; Sujani Kakumanu; et al. 2022. The Journal of Allergy and Clinical Immunology: In Practicejournal articleCited in: Allergy, Immunology, and Medical Genetics
- Clinical Variant Reclassification in Hereditary Disease Genetic TestingYuya Kobayashi; Elaine Chen; Flavia M. Facio; et al. 2024. JAMA Network Openjournal articleCited in: Allergy, Immunology, and Medical Genetics (passage 1); Allergy, Immunology, and Medical Genetics (passage 2)
- Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populationsNiall J. Lennon; Leah C. Kottyan; Christopher Kachulis; et al. 2024. Nature Medicinejournal articleCited in: Allergy, Immunology, and Medical Genetics
- Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysisDaniil Lisik; Saliha Selin Özuygur Ermis; Gregorio Paolo Milani; et al. 2025. European Respiratory Reviewjournal articleCited in: Allergy, Immunology, and Medical Genetics
- Challenges in clinical translation of polygenic risk score analyses: A systematic reviewDiana Martínez-Minguet; René Noel; Alberto G. Simón; et al. 2026. Genetics in Medicinejournal articleCited in: Allergy, Immunology, and Medical Genetics
- Diagnostic accuracy of large language models for rare diseases: a systematic review and meta-analysisMinh-Ha Nguyen; Chih-Ting Yang; Thomas A. Cassini; et al. 2026. npj Digital Medicinejournal articleCited in: Allergy, Immunology, and Medical Genetics (passage 1); Allergy, Immunology, and Medical Genetics (passage 2)
- A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritizationGiovanna Nicora; Susanna Zucca; Ivan Limongelli; et al. 2022. Scientific Reportsjournal articleCited in: Allergy, Immunology, and Medical Genetics
- Validation of 3 Computer-Aided Facial Phenotyping Tools (DeepGestalt, GestaltMatcher, and D-Score): Comparative Diagnostic Accuracy StudyAlisa Maria Vittoria Reiter; Jean Tori Pantel; Magdalena Danyel; et al. 2024. Journal of Medical Internet Researchjournal articleCited in: Allergy, Immunology, and Medical Genetics
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular PathologySue Richards; Nazneen Aziz; Sherri Bale; et al. 2015. Genetics in Medicinejournal articleCited in: Allergy, Immunology, and Medical Genetics
- Richards et al., 2015Cited in: Allergy, Immunology, and Medical Genetics
- AI-based multimodal integration of genomics and electronic health recordsRasika Venkatesh; Marylyn D. Ritchie. 2026. Nature Reviews Geneticsjournal articleCited in: Allergy, Immunology, and Medical Genetics
- Yahya et al., 2025Cited in: Allergy, Immunology, and Medical Genetics
Surgical Subspecialties
References
- 2024 ACG/ASGE Quality Indicators for ColonoscopyCited in: Surgical Subspecialties
- AAO-HNS Clinical Practice Guideline on ARHL (2024)Cited in: Surgical Subspecialties
- AAOS Position Statement 1193Cited in: Surgical Subspecialties
- AGA Clinical Practice Update on AI in Polyp DiagnosisCited in: Surgical Subspecialties
- AGA Living Clinical Practice Guideline on CADe-Assisted ColonoscopyCited in: Surgical Subspecialties
- Ali et al., 2024Cited in: Surgical Subspecialties
- AUA 2025Cited in: Surgical Subspecialties
- AUA 2025 reportCited in: Surgical Subspecialties
- AUA Advocacy, June 2024Cited in: Surgical Subspecialties
- AUA has no standalone AI clinical practice guidelineCited in: Surgical Subspecialties
- AUA Policy and Position StatementsCited in: Surgical Subspecialties
- AUA Privacy PolicyCited in: Surgical Subspecialties
- AUA/SUO Early Detection of Prostate Cancer GuidelineCited in: Surgical Subspecialties
- American Academy of Otolaryngology–Head and Neck Surgery (AAO‐HNS) Report on Artificial IntelligenceNoel F. Ayoub; Anaïs Rameau; Michael J. Brenner; et al. 2025. Otolaryngology–Head and Neck Surgeryjournal articleCited in: Surgical Subspecialties
- Comparative Efficacy of Different AI Systems for Polyp Detection by Size During Colonoscopy: Systematic Review and Network Meta-AnalysisLing Ba; Yaxin Qi; Xinrui Lv; et al. 2026. Journal of Medical Internet Researchjournal articleCited in: Surgical Subspecialties
- External Validation of an Artificial Intelligence Algorithm Using Biparametric MRI and Its Simulated Integration with Conventional PI-RADS for Prostate Cancer DetectionMason J. Belue; Vaneeza Mukhtar; Roopa Ram; et al. 2025. Academic Radiologyjournal articleCited in: Surgical Subspecialties
- Reinforcement learning based automated anesthesia system for gastrointestinal endoscopy with a multicenter randomized trialHai-Long Bing; Yan Wang; Shi-Ying Li; et al. 2026. npj Digital Medicinejournal articleCited in: Surgical Subspecialties
- Cochlear Nucleus Nexa SystemCited in: Surgical Subspecialties
- COLO-DETECT, 2024Cited in: Surgical Subspecialties
- Consensus Recommendations on Surgical Video DataCited in: Surgical Subspecialties
- Adenoma Detection Rate and Risk of Colorectal Cancer and DeathDouglas A. Corley; Christopher D. Jensen; Amy R. Marks; et al. 2014. New England Journal of Medicinejournal articleCited in: Surgical Subspecialties
- Defining Digital Surgery White PaperCited in: Surgical Subspecialties
- DEN200055Cited in: Surgical Subspecialties
- Educational content on AI/MLCited in: Surgical Subspecialties
- EUS/AUA 2025Cited in: Surgical Subspecialties
- FDA DEN230027 decision summaryCited in: Surgical Subspecialties
- FDA K231862Cited in: Surgical Subspecialties
- FDA K241232Cited in: Surgical Subspecialties
- FDA K250725Cited in: Surgical Subspecialties
- FDA recall Z-0127-2024Cited in: Surgical Subspecialties
- FDA, DEN200080Cited in: Surgical Subspecialties
- FDA, DEN240068Cited in: Surgical Subspecialties
- FDA, K221624Cited in: Surgical Subspecialties
- Computer aided detection and diagnosis of polyps in adult patients undergoing colonoscopy: a living clinical practice guidelineFarid Foroutan; Per Olav Vandvik; Lise M Helsingen; et al. 2025. BMJjournal articleCited in: Surgical Subspecialties
- Healthcare, 2025Cited in: Surgical Subspecialties
- AI-based large-scale screening of gastric cancer from noncontrast CT imagingCan Hu; Yingda Xia; Zhilin Zheng; et al. 2025. Nature Medicinejournal articleCited in: Surgical Subspecialties
- Informatics and AI CommitteeCited in: Surgical Subspecialties
- J&J, 2025Cited in: Surgical Subspecialties
- Machine Learning Achieves Pathologist-Level Celiac Disease DiagnosisFlorian Jaeckle; James Denholm; Benjamin Schreiber; et al. 2025. NEJM AIjournal articleCited in: Surgical Subspecialties
- K211326Cited in: Surgical Subspecialties
- K211951Cited in: Surgical Subspecialties
- Position statement from the society of University surgeons, surgical education committee: Artificial intelligence in surgical training for medical students, residents, and fellowsDivya Kewalramani; Randeep S. Jawa; Colin A. Martin; et al. 2026. Surgeryjournal articleCited in: Surgical Subspecialties
- Artificial intelligence assisted colorectal lesion detection in private practices a randomized controlled studyThomas J. Lux; Zita Saßmannshausen; Ioannis Kafetzis; et al. 2026. npj Digital Medicinejournal articleCited in: Surgical Subspecialties
- Mittmann et al., 2025Cited in: Surgical Subspecialties
- npj Digital Medicine, 2025Cited in: Surgical Subspecialties
- Policy Statement on the Use of AI in NeurosurgeryCited in: Surgical Subspecialties
- Position Statement on AI in Surgical TrainingCited in: Surgical Subspecialties
- Position Statement on Priorities for AI in GI EndoscopyCited in: Surgical Subspecialties
- Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized TrialAlessandro Repici; Matteo Badalamenti; Roberta Maselli; et al. 2020. Gastroenterologyjournal articleCited in: Surgical Subspecialties
- Quality Indicators for ColonoscopyDouglas K. Rex; Joseph C. Anderson; Lynn F. Butterly; et al. 2024. American Journal of Gastroenterologyjournal articleCited in: Surgical Subspecialties
- AGA Clinical Practice Update on the Role of Artificial Intelligence in Colon Polyp Diagnosis and Management: CommentaryJason Samarasena; Dennis Yang; Tyler M. Berzin. 2023. Gastroenterologyjournal articleCited in: Surgical Subspecialties
- Effectiveness of the GI Genius Computer-Aided Detection System Versus Standard Colonoscopy: A Systematic Review and Meta-Analysis of Randomized Controlled TrialsAliya Sattar; Arifa Sattar; Muhammad Haris Khan; et al. 2025. Cureusjournal articleCited in: Surgical Subspecialties
- Detection of undiagnosed liver cirrhosis via AI-enabled electrocardiogram: a pragmatic, cluster-randomized clinical trialDouglas A. Simonetto; David Rushlow; Kan Liu; et al. 2025. Nature Medicinejournal articleCited in: Surgical Subspecialties
- Artificial Intelligence–Assisted Colonoscopy for Polyp DetectionSaeed Soleymanjahi; Jack Huebner; Lina Elmansy; et al. 2024. Annals of Internal Medicinejournal articleCited in: Surgical Subspecialties
- Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinomaBolin Song; Amaury Leroy; Kailin Yang; et al. 2025. eBioMedicinejournal articleCited in: Surgical Subspecialties
- AGA Living Clinical Practice Guideline on Computer-Aided Detection–Assisted ColonoscopyShahnaz Sultan; Dennis L. Shung; Jennifer M. Kolb; et al. 2025. Gastroenterologyjournal articleCited in: Surgical Subspecialties
- TOBY company release, 2025Cited in: Surgical Subspecialties
- Requirements for AI Development and Reporting for MRI Prostate Cancer Detection in Biopsy-Naive Men: PI-RADS Steering Committee, Version 1.0Baris Turkbey; Henkjan Huisman; Andriy Fedorov; et al. 2025. Radiologyjournal articleCited in: Surgical Subspecialties
- Wang et al., 2026Cited in: Surgical Subspecialties
- Artificial Intelligence–Assisted Colonoscopy in Real-World Clinical Practice: A Systematic Review and Meta-AnalysisMike Tzuhen Wei; Shmuel Fay; Diana Yung; et al. 2024. Clinical and Translational Gastroenterologyjournal articleCited in: Surgical Subspecialties
- World Journal of Urology, 2025Cited in: Surgical Subspecialties
- An Artificial Intelligence System for the Detection of Bladder Cancer via Cystoscopy: A Multicenter Diagnostic StudyShaoxu Wu; Xiong Chen; Jiexin Pan; et al. 2022. JNCI: Journal of the National Cancer Institutejournal articleCited in: Surgical Subspecialties
- No Effect of Computer-Aided Diagnosis on Colonoscopic Adenoma Detection in a Large Pragmatic Multicenter Randomized StudyKatharina Zimmermann-Fraedrich; Susanne Sehner; Thomas Rösch; et al. 2026. American Journal of Gastroenterologyjournal articleCited in: Surgical Subspecialties
Part III: Clinical Implementation and Evaluation
Evaluating AI Clinical Decision Support Systems
References
- Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability ChallengesQaiser Abbas; Woonyoung Jeong; Seung Won Lee. 2025. Healthcarejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trialAmbrose Agweyu; Paul Mwaniki; Vaishnavi Menon; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Ajmal et al., 2026, preprintCited in: Evaluating AI Clinical Decision Support Systems
- Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendationsJoseph E Alderman; Joanne Palmer; Elinor Laws; et al. 2025. The Lancet Digital Healthjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- ARISE MAST methodologyCited in: Evaluating AI Clinical Decision Support Systems
- Principles to guide clinical AI readiness and move from benchmarks to real-world evaluationTej D. Azad; Harlan M. Krumholz; Suchi Saria. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Mechanistic interpretability of reinforcement learning in Medicaid care coordinationSanjay Basu; Sadiq Patel; Parth Sheth; et al. 2026. BMJ Health & Care Informaticsjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Reliability of LLMs as medical assistants for the general public: a randomized preregistered studyAndrew M. Bean; Rebecca Elizabeth Payne; Guy Parsons; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Fidelity of Medical Reasoning in Large Language ModelsSuhana Bedi; Yixing Jiang; Philip Chung; et al. 2025. JAMA Network Openjournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1); Evaluating AI Clinical Decision Support Systems (passage 2); Evaluating AI Clinical Decision Support Systems (passage 3); Evaluating AI Clinical Decision Support Systems (passage 4)
- Testing and Evaluation of Health Care Applications of Large Language ModelsSuhana Bedi; Yutong Liu; Lucy Orr-Ewing; et al. 2025. JAMAjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- MedHELM: Holistic evaluation of large language models in medicineSuhana Bedi. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Building safer clinical agents: the case for residency-level benchmarks in medical artificial intelligenceKameron C Black. 2026. The Lancet Digital Healthjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Brankovic et al., NPJ Digital Medicine, 2025Cited in: Evaluating AI Clinical Decision Support Systems
- Performance of a large language model on the reasoning tasks of a physicianPeter G. Brodeur; Thomas A. Buckley; Zahir Kanjee; et al. 2026. Sciencejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- CDC ACIP GRADE Handbook, 2024Cited in: Evaluating AI Clinical Decision Support Systems
- LLM-assisted systematic review of large language models in clinical medicineSully F. Chen; Anton Alyakin; Andreas Seas; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Chen et al., 2026, preprintCited in: Evaluating AI Clinical Decision Support Systems (passage 1); Evaluating AI Clinical Decision Support Systems (passage 2)
- Cochrane RoB 2Cited in: Evaluating AI Clinical Decision Support Systems (passage 1)Recommended in: Evaluating AI Clinical Decision Support Systems (passage 2)
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methodsGary S Collins; Karel G M Moons; Paula Dhiman; et al. 2024. BMJjournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1)Recommended in: Evaluating AI Clinical Decision Support Systems (passage 2)
- Cook et al., Mayo Clinic Proceedings: Digital Health, 2026Cited in: Evaluating AI Clinical Decision Support Systems
- Patient-reported outcome and experience domains for diagnostic excellence: a scoping review to inform future measure developmentVadim Dukhanin; Mary Jo Gamper; Kelly T. Gleason; et al. 2024. Quality of Life Researchjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- FDACited in: Evaluating AI Clinical Decision Support Systems
- FDA CDS Guidance, January 2026Cited in: Evaluating AI Clinical Decision Support Systems
- FDA Draft Guidance, January 2025Cited in: Evaluating AI Clinical Decision Support Systems
- FDA PCCP guidanceCited in: Evaluating AI Clinical Decision Support Systems
- FDA, 2024Cited in: Evaluating AI Clinical Decision Support Systems
- FDA, 2025Cited in: Evaluating AI Clinical Decision Support Systems
- Toward a test of medical AI superintelligenceEthan Goh; David Wu; Chase Walton; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trialJessie Gommers; Veronica Hernström; Viktoria Josefsson; et al. 2026. The Lancetjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- HealthBenchCited in: Evaluating AI Clinical Decision Support Systems
- Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1.Miguel A. Hernán; James M. Robins. 2016. American Journal of Epidemiologyjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- AI can reason like a physician—what comes next?Ashley M. Hopkins; Erik Cornelisse. 2026. Sciencejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- in previewCited in: Evaluating AI Clinical Decision Support Systems
- Measuring the Impact of AI in the Diagnosis of Hospitalized PatientsSarah Jabbour; David Fouhey; Stephanie Shepard; et al. 2023. JAMAjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Jain & Wallace, 2019Cited in: Evaluating AI Clinical Decision Support Systems
- MedAgentBench: A Virtual EHR Environment to Benchmark Medical LLM AgentsYixing Jiang; Kameron C. Black; Gloria Geng; et al. 2025. NEJM AIjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- An evaluation framework for clinical use of large language models in patient interaction tasksShreya Johri; Jaehwan Jeong; Benjamin A. Tran; et al. 2025. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Geographic Distribution of US Cohorts Used to Train Deep Learning AlgorithmsAmit Kaushal; Russ Altman; Curt Langlotz. 2020. JAMAjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Structured reasoning failures compromise LLM interpretation of clinical oncology notesMatthew W. Kenaston; Umair Ayub; Mihir Parmar; et al. 2026. npj Digital Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Clinical Large Language Model Evaluation by Expert Review (CLEVER): Framework Development and ValidationVeysel Kocaman; Mustafa Aytuğ Kaya; Andrei Marian Feier; et al. 2025. JMIR AIjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided DetectionConstance D. Lehman; Robert D. Wellman; Diana S. M. Buist; et al. 2015. JAMA Internal Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1); Evaluating AI Clinical Decision Support Systems (passage 2)
- Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extensionXiaoxuan Liu; Samantha Cruz Rivera; David Moher; et al. 2020. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1)Recommended in: Evaluating AI Clinical Decision Support Systems (passage 2)
- The medical algorithmic auditXiaoxuan Liu; Ben Glocker; Melissa M McCradden; et al. 2022. The Lancet Digital Healthjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Towards conversational artificial intelligence for disease managementValentin Liévin; Anil Palepu; Wei-Hung Weng; et al. 2026. Naturejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Assessment of Large Language Models in Clinical Reasoning: A Novel Benchmarking StudyLiam G. McCoy; Rajiv Swamy; Nidhish Sagar; et al. 2025. NEJM AIjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- McCradden et al., 2025Cited in: Evaluating AI Clinical Decision Support Systems
- Miller, 2024, preprintCited in: Evaluating AI Clinical Decision Support Systems
- PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methodsKarel G M Moons; Johanna A A Damen; Tabea Kaul; et al. 2025. BMJjournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1)Recommended in: Evaluating AI Clinical Decision Support Systems (passage 2)
- Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studiesMyura Nagendran; Yang Chen; Christopher A Lovejoy; et al. 2020. BMJjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- OpenAI et al., 2025, preprintCited in: Evaluating AI Clinical Decision Support Systems
- OpenAI, HealthBench Professional, 2026Cited in: Evaluating AI Clinical Decision Support Systems
- Osmanodja et al., 2026Cited in: Evaluating AI Clinical Decision Support Systems
- Addressing benchmarking gaps in large language models for health and medicine with dynamic red-teamingJiazhen Pan; Bailiang Jian; Paul Hager; et al. 2026. Nature Healthjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- public repositoryCited in: Evaluating AI Clinical Decision Support Systems
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical OutcomesBashar Ramadan; Ming-Chieh Liu; Michael C. Burkhart; et al. 2025. JAMA Network Openjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Rao et al., 2026Cited in: Evaluating AI Clinical Decision Support Systems (passage 1); Evaluating AI Clinical Decision Support Systems (passage 2)
- Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trialSarah C. Rossetti; Patricia C. Dykes; Chris Knaplund; et al. 2025. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Author Correction: Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trialSarah C. Rossetti; Patricia C. Dykes; Chris Knaplund; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Impact of LLM assistance on physician decision-making: a multi-country randomized controlled trialNicholas Rounding; Luthfi Saiful Arif; Janine Berg; et al. 2026. npj Digital Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Stop explaining black box machine learning models for high stakes decisions and use interpretable models insteadCynthia Rudin. 2019. Nature Machine Intelligencejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Lessons from deploying the ChatEHR system at Stanford MedicineNigam H. Shah; Niraj Sehgal; Euan A. Ashley; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1); Evaluating AI Clinical Decision Support Systems (passage 2)
- Physicians and artificial intelligence diverge in evaluating large language models on real clinical casesPeilun Shi; Jian Li; Ziqi Yang; et al. 2026. npj Digital Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligenceViknesh Sounderajah; Ahmad Guni; Xiaoxuan Liu; et al. 2025. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1)Recommended in: Evaluating AI Clinical Decision Support Systems (passage 2)
- Sterne et al., 2016Cited in: Evaluating AI Clinical Decision Support Systems (passage 1)Recommended in: Evaluating AI Clinical Decision Support Systems (passage 2)
- A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperformAdarsh Subbaswamy; Berkman Sahiner; Nicholas Petrick; et al. 2024. npj Digital Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- supplementary evidence tablesCited in: Evaluating AI Clinical Decision Support Systems
- Framework for bias evaluation in large language models in healthcare settingsTara Templin; Sophia Fort; Prasanna Padmanabham; et al. 2025. npj Digital Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- A scoping review of silent trials for medical artificial intelligenceLana Tikhomirov; Carolyn Semmler; Noah Prizant; et al. 2026. Nature Healthjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AIBaptiste Vasey; Myura Nagendran; Bruce Campbell; et al. 2022. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1)Recommended in: Evaluating AI Clinical Decision Support Systems (passage 2)
- General-purpose large language models outperform specialized clinical AI tools on medical benchmarksKrithik Vishwanath; Anton Alyakin; Mrigayu Ghosh; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- QUADAS-3: A Revised Tool for the Quality Assessment of Diagnostic Test Accuracy StudiesPenny F. Whiting; Eve Tomlinson; Anne W.S. Rutjes; et al. 2026. Annals of Internal Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1)Recommended in: Evaluating AI Clinical Decision Support Systems (passage 2)
- Human evaluators vs. LLM-as-a-Judge: toward scalable evaluation of GenAI in global healthGwydion Williams; Samuel Rutunda; Floris Nzabakira; et al. 2026. npj Digital Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction ModelAndrew Wong. 2026. JAMA Network Openjournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Wong et al., 2021Cited in: Evaluating AI Clinical Decision Support Systems (passage 1); Evaluating AI Clinical Decision Support Systems (passage 2); Evaluating AI Clinical Decision Support Systems (passage 3)
- How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvalsEric Wu; Kevin Wu; Roxana Daneshjou; et al. 2021. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Wu et al., 2025, preprintCited in: Evaluating AI Clinical Decision Support Systems
- A five-phase evaluation framework for diagnostic and predictive medical artificial intelligenceZichen Ye; Yue Chen; Xuefeng Huang; et al. 2026. npj Digital Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Effect of a clinical decision support system on stroke care quality and outcomes in patients with acute ischaemic stroke (GOLDEN BRIDGE II): cluster randomised clinical trialXinmiao Zhang; Lingling Ding; Jing Jing; et al. 2026. BMJjournal articleCited in: Evaluating AI Clinical Decision Support Systems (passage 1); Evaluating AI Clinical Decision Support Systems (passage 2)
- On-premise medical AI agents for reliable clinical decision-makingLi Zhang; Georg Wölflein; Dyke Ferber; et al. 2026. Nature Medicinejournal articleCited in: Evaluating AI Clinical Decision Support Systems
- Zhang et al., 2024Cited in: Evaluating AI Clinical Decision Support Systems
Recommended reading
- 510(k) ClearancesRecommended in: Evaluating AI Clinical Decision Support Systems
- ACR-SIIM Practice Parameter for Imaging AIRecommended in: Evaluating AI Clinical Decision Support Systems
- AMA Augmented Intelligence in MedicineRecommended in: Evaluating AI Clinical Decision Support Systems
- AMA eight-step health-system AI governance toolkitRecommended in: Evaluating AI Clinical Decision Support Systems
- crfm.stanford.edu/helm/medhelmRecommended in: Evaluating AI Clinical Decision Support Systems
- Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extensionSamantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; et al. 2020. Nature Medicinejournal articleRecommended in: Evaluating AI Clinical Decision Support Systems
- The TRIPOD-LLM reporting guideline for studies using large language modelsJack Gallifant; Majid Afshar; Saleem Ameen; et al. 2025. Nature Medicinejournal articleRecommended in: Evaluating AI Clinical Decision Support Systems
- GRADERecommended in: Evaluating AI Clinical Decision Support Systems
- Consensus framework for the validation of generative AI: call for collaborators on the Validation AccordsBright Huo; David Chartash; Lincoln Tsang; et al. 2026. Nature Medicinejournal articleRecommended in: Evaluating AI Clinical Decision Support Systems
- MicrosoftRecommended in: Evaluating AI Clinical Decision Support Systems
- NICE Evidence Standards FrameworkRecommended in: Evaluating AI Clinical Decision Support Systems
- scheduled to take effect October 1, 2026Recommended in: Evaluating AI Clinical Decision Support Systems
- University of ChicagoRecommended in: Evaluating AI Clinical Decision Support Systems
Medical Ethics, Bias, and Health Equity
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- Noori et al., 2025, preprintCited in: Medical Ethics, Bias, and Health Equity
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Medical Ethics, Bias, and Health Equity (passage 1); Medical Ethics, Bias, and Health Equity (passage 2); Medical Ethics, Bias, and Health Equity (passage 3); Medical Ethics, Bias, and Health Equity (passage 4); Medical Ethics, Bias, and Health Equity (passage 5); Medical Ethics, Bias, and Health Equity (passage 6); Medical Ethics, Bias, and Health Equity (passage 7); Medical Ethics, Bias, and Health Equity (passage 8); Medical Ethics, Bias, and Health Equity (passage 9)
- Addressing Bias in Artificial Intelligence in Health CareRavi B. Parikh; Stephanie Teeple; Amol S. Navathe. 2019. JAMAjournal articleCited in: Medical Ethics, Bias, and Health Equity
- Persad et al., Lancet, 2009Cited in: Medical Ethics, Bias, and Health Equity
- An algorithmic approach to reducing unexplained pain disparities in underserved populationsEmma Pierson; David M. Cutler; Jure Leskovec; et al. 2021. Nature Medicinejournal articleCited in: Medical Ethics, Bias, and Health Equity (passage 1); Medical Ethics, Bias, and Health Equity (passage 2)
- Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populationsLaleh Seyyed-Kalantari; Haoran Zhang; Matthew B. A. McDermott; et al. 2021. Nature Medicinejournal articleCited in: Medical Ethics, Bias, and Health Equity (passage 1); Medical Ethics, Bias, and Health Equity (passage 2)
- Racial Bias in Pulse Oximetry MeasurementMichael W. Sjoding; Robert P. Dickson; Theodore J. Iwashyna; et al. 2020. New England Journal of Medicinejournal articleCited in: Medical Ethics, Bias, and Health Equity
- High-performance medicine: the convergence of human and artificial intelligenceEric J. Topol. 2019. Nature Medicinejournal articleCited in: Medical Ethics, Bias, and Health Equity (passage 1); Medical Ethics, Bias, and Health Equity (passage 2)
- Machine learning algorithm validation with a limited sample sizeAndrius Vabalas; Emma Gowen; Ellen Poliakoff; et al. 2019. PLOS ONEjournal articleCited in: Medical Ethics, Bias, and Health Equity
- Machine learning in medicine: Addressing ethical challengesVayena E, Blasimme A, Cohen IG. 2018. PLOS MedicineperspectiveCited in: Medical Ethics, Bias, and Health Equity (passage 1); Medical Ethics, Bias, and Health Equity (passage 2)
- Wong et al., 2021Cited in: Medical Ethics, Bias, and Health Equity
Privacy, HIPAA, and Patient Data Security
References
- Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)European Parliament and Council of the European Union. 2016. Official Journal of the European UnionregulationCited in: Privacy, HIPAA, and Patient Data Security
- Health Breach Notification RuleFederal Trade Commission. FTCCited in: Privacy, HIPAA, and Patient Data Security
- Health Breach Notification Rule (16 CFR Part 318)Federal Trade Commission. eCFRregulationCited in: Privacy, HIPAA, and Patient Data Security
- FTC Finalizes Order with Flo Health, a Fertility-Tracking App that Shared Sensitive Health Data with Facebook, Google, and OthersFederal Trade Commission. 2021. FTCCited in: Privacy, HIPAA, and Patient Data Security
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- FTC Enforcement Action to Bar GoodRx from Sharing Consumers’ Sensitive Health Info for AdvertisingFederal Trade Commission. 2023. FTCCited in: Privacy, HIPAA, and Patient Data Security
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- IBMIBMCited in: Privacy, HIPAA, and Patient Data Security
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- HTI-1 Final RuleOffice of the National Coordinator for Health Information Technology. ONCCited in: Privacy, HIPAA, and Patient Data Security
- Privacy in the age of medical big dataPrice WN II, Cohen IG. 2019. Nature MedicinereviewCited in: Privacy, HIPAA, and Patient Data Security (passage 1); Privacy, HIPAA, and Patient Data Security (passage 2); Privacy, HIPAA, and Patient Data Security (passage 3); Privacy, HIPAA, and Patient Data Security (passage 4)
- Membership Inference Attacks Against Machine Learning ModelsShokri R, Stronati M, Song C, et al. 2017. 2017 IEEE Symposium on Security and Privacy (SP)conference-paperCited in: Privacy, HIPAA, and Patient Data Security (passage 1); Privacy, HIPAA, and Patient Data Security (passage 2)
- Simple Demographics Often Identify People UniquelySweeney L. 2000. Carnegie Mellon University, Data Privacy Working Paper 3Cited in: Privacy, HIPAA, and Patient Data Security
- Only You, Your Doctor, and Many Others May KnowSweeney L. 2015. Technology ScienceCited in: Privacy, HIPAA, and Patient Data Security (passage 1); Privacy, HIPAA, and Patient Data Security (passage 2)
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- Is a software vendor a business associate of a covered entity?U.S. Department of Health and Human Services. 2002. HHSagency-guidanceCited in: Privacy, HIPAA, and Patient Data Security
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- Machine learning in medicine: Addressing ethical challengesVayena E, Blasimme A, Cohen IG. 2018. PLOS MedicineperspectiveCited in: Privacy, HIPAA, and Patient Data Security (passage 1); Privacy, HIPAA, and Patient Data Security (passage 2)
Clinical AI Safety and Risk Management
References
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- Principles to guide clinical AI readiness and move from benchmarks to real-world evaluationTej D. Azad; Harlan M. Krumholz; Suchi Saria. 2026. Nature Medicinejournal articleCited in: Clinical AI Safety and Risk Management
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- FDA Draft Guidance, January 2025Cited in: Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2)
- FDA PCCP guidanceCited in: Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2)
- FDA, 2025Cited in: Clinical AI Safety and Risk Management
- FDA, Mandatory Reporting RequirementsCited in: Clinical AI Safety and Risk Management
- The Clinician and Dataset Shift in Artificial IntelligenceSamuel G. Finlayson; Adarsh Subbaswamy; Karandeep Singh; et al. 2021. New England Journal of Medicinejournal articleCited in: Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2)
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- Joint Commission and CHAI, September 2025Cited in: Clinical AI Safety and Risk Management
- Key challenges for delivering clinical impact with artificial intelligenceChristopher J. Kelly; Alan Karthikesalingam; Mustafa Suleyman; et al. 2019. BMC Medicinejournal articleCited in: Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2); Clinical AI Safety and Risk Management (passage 3)
- Khullar, 2025Cited in: Clinical AI Safety and Risk Management
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- Early Recalls and Clinical Validation Gaps in Artificial Intelligence–Enabled Medical DevicesBranden Lee; Patrick Kramer; Sara Sandri; et al. 2025. JAMA Health Forumjournal articleCited in: Clinical AI Safety and Risk Management
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- Assessment of Large Language Models in Clinical Reasoning: A Novel Benchmarking StudyLiam G. McCoy; Rajiv Swamy; Nidhish Sagar; et al. 2025. NEJM AIjournal articleCited in: Clinical AI Safety and Risk Management
- OpenAI, 2025Cited in: Clinical AI Safety and Risk Management
- Pierre et al., 2025Cited in: Clinical AI Safety and Risk Management
- Care to Explain? AI Explanation Types Differentially Impact Chest Radiograph Diagnostic Performance and Physician Trust in AIDrew Prinster; Amama Mahmood; Suchi Saria; et al. 2024. Radiologyjournal articleCited in: Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2); Clinical AI Safety and Risk Management (passage 3)
- Ross & Swetlitz, 2018Cited in: Clinical AI Safety and Risk Management
- Development and validation of a clinical wearable deep learning based continuous inhospital deterioration prediction modelMichael R. Scheid; Beth Friedmann; Michael Oppenheim; et al. 2025. Nature Communicationsjournal articleCited in: Clinical AI Safety and Risk Management
- A Classification of Safety Risks in Medical AIBin Sheng; Jiaying Song; Nigam H. Shah; et al. 2026. NEJM AIjournal articleCited in: Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2)
- State of Clinical AI Report 2026Cited in: Clinical AI Safety and Risk Management
- High-performance medicine: the convergence of human and artificial intelligenceEric J. Topol. 2019. Nature Medicinejournal articleCited in: Clinical AI Safety and Risk Management
- WHO, 2025Cited in: Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2)
- Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma RecognitionJulia K. Winkler; Christine Fink; Ferdinand Toberer; et al. 2019. JAMA Dermatologyjournal articleCited in: Clinical AI Safety and Risk Management
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- Heterogeneity and predictors of the effects of AI assistance on radiologistsFeiyang Yu; Alex Moehring; Oishi Banerjee; et al. 2024. Nature Medicinejournal articleCited in: Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2); Clinical AI Safety and Risk Management (passage 3)
- Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional studyJohn R. Zech; Marcus A. Badgeley; Manway Liu; et al. 2018. PLOS Medicinejournal articleCited in: Clinical AI Safety and Risk Management (passage 1); Clinical AI Safety and Risk Management (passage 2)
- On-premise medical AI agents for reliable clinical decision-makingLi Zhang; Georg Wölflein; Dyke Ferber; et al. 2026. Nature Medicinejournal articleCited in: Clinical AI Safety and Risk Management
Integration into Clinical Workflow
References
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- Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational studyKrzysztof Budzyń; Marcin Romańczyk; Diana Kitala; et al. 2025. The Lancet Gastroenterology & Hepatologyjournal articleCited in: Integration into Clinical Workflow (passage 1); Integration into Clinical Workflow (passage 2); Integration into Clinical Workflow (passage 3); Integration into Clinical Workflow (passage 4)
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- FDA DEN180001Cited in: Integration into Clinical Workflow
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- Kohn et al., 2000Cited in: Integration into Clinical Workflow
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- Prospective evaluation of a large language model clinical decision support system in the emergency departmentLiron Leibovitch; Adi Ahituv; Alon Gorenshtein; et al. 2026. Nature Medicinejournal articleCited in: Integration into Clinical Workflow
- Automation Bias in Large Language Model Assisted Diagnostic Reasoning Among AI-Trained PhysiciansIhsan Ayyub Qazi; Ayesha Ali; Asad Ullah Khawaja; et al. 2025preprintCited in: Integration into Clinical Workflow
- Presenting machine learning model information to clinical end users with model facts labelsMark P. Sendak; Michael Gao; Nathan Brajer; et al. 2020. npj Digital Medicinejournal articleCited in: Integration into Clinical Workflow
- Sendak et al., 2020Cited in: Integration into Clinical Workflow (passage 1); Integration into Clinical Workflow (passage 2)
- State of Clinical AI Report 2026Cited in: Integration into Clinical Workflow
- Overriding of Drug Safety Alerts in Computerized Physician Order EntryH. van der Sijs; J. Aarts; A. Vulto; et al. 2006. Journal of the American Medical Informatics Associationjournal articleCited in: Integration into Clinical Workflow (passage 1); Integration into Clinical Workflow (passage 2)
- Human–large language model collaboration in clinical medicine: a systematic review and meta-analysisGuoyong Wang; Kaijun Zhang; Jiyue Jiang; et al. 2026. npj Digital Medicinejournal articleCited in: Integration into Clinical Workflow
- WHO, 2025Cited in: Integration into Clinical Workflow
- Wong et al., 2021Cited in: Integration into Clinical Workflow (passage 1); Integration into Clinical Workflow (passage 2)
- Initial lessons from real-world implementation of an AI-agent eye clinic in ChinaTao Yan; Di Zhang; Luxiao Chen; et al. 2026. Nature Medicinejournal articleCited in: Integration into Clinical Workflow
Physician AI Liability and Regulatory Compliance
References
- 510(k) ClearancesCited in: Physician AI Liability and Regulatory Compliance
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- ACR-SIIM Practice Parameter for Imaging AICited in: Physician AI Liability and Regulatory Compliance
- Arizona HB 2175Cited in: Physician AI Liability and Regulatory Compliance
- An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome predictionZachi I Attia; Peter A Noseworthy; Francisco Lopez-Jimenez; et al. 2019. The Lancetjournal articleCited in: Physician AI Liability and Regulatory Compliance
- Beam and Kohane, 2018Cited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2); Physician AI Liability and Regulatory Compliance (passage 3)
- Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?Brett K. Beaulieu-Jones; William Yuan; Gabriel A. Brat; et al. 2021. npj Digital Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance
- Randomized Study of the Impact of AI on Perceived Legal Liability for RadiologistsMichael H. Bernstein; Brian Sheppard; Michael A. Bruno; et al. 2025. NEJM AIjournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- The radiologist–AI workflow and the risk of medical malpractice claimsMichael H. Bernstein; Brian Sheppard; Michael A. Bruno; et al. 2026. Nature Healthjournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- Computer-Aided Detection as Evidence in the Courtroom: Potential Implications of an Appellate Court's RulingR. James Brenner; Michael J. Ulissey; Ronald M. Wilt. 2006. American Journal of Roentgenologyjournal articleCited in: Physician AI Liability and Regulatory Compliance
- Implementing Machine Learning in Health Care — Addressing Ethical ChallengesDanton S. Char; Nigam H. Shah; David Magnus. 2018. New England Journal of Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2); Physician AI Liability and Regulatory Compliance (passage 3); Physician AI Liability and Regulatory Compliance (passage 4); Physician AI Liability and Regulatory Compliance (passage 5); Physician AI Liability and Regulatory Compliance (passage 6)
- Chew, 2025Cited in: Physician AI Liability and Regulatory Compliance
- Colorado SB 26-189Cited in: Physician AI Liability and Regulatory Compliance
- Disparities in dermatology AI performance on a diverse, curated clinical image setRoxana Daneshjou; Kailas Vodrahalli; Roberto A. Novoa; et al. 2022. Science Advancesjournal articleCited in: Physician AI Liability and Regulatory Compliance
- EUR-Lex procedure 2022/0303/CODCited in: Physician AI Liability and Regulatory Compliance
- European Commission AI Act FAQCited in: Physician AI Liability and Regulatory Compliance
- Executive Order 14365Cited in: Physician AI Liability and Regulatory Compliance
- FDA CDS Guidance, January 2026Cited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- FDA Draft Guidance, January 2025Cited in: Physician AI Liability and Regulatory Compliance
- FDA PCCP guidanceCited in: Physician AI Liability and Regulatory Compliance
- FDA, August 2026Cited in: Physician AI Liability and Regulatory Compliance
- Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcareJean Feng; Rachael V. Phillips; Ivana Malenica; et al. 2022. npj Digital Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance
- Adversarial attacks on medical machine learningSamuel G. Finlayson; John D. Bowers; Joichi Ito; et al. 2019. Sciencejournal articleCited in: Physician AI Liability and Regulatory Compliance
- Illinois Public Act 104-0054Cited in: Physician AI Liability and Regulatory Compliance
- Key challenges for delivering clinical impact with artificial intelligenceChristopher J. Kelly; Alan Karthikesalingam; Mustafa Suleyman; et al. 2019. BMC Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- Early Recalls and Clinical Validation Gaps in Artificial Intelligence–Enabled Medical DevicesBranden Lee; Patrick Kramer; Sara Sandri; et al. 2025. JAMA Health Forumjournal articleCited in: Physician AI Liability and Regulatory Compliance
- Generative AI in Medicine — Evaluating Progress and ChallengesThomas M. Maddox; Peter Embí; Jackie Gerhart; et al. 2025. New England Journal of Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- MDCG 2025-6Cited in: Physician AI Liability and Regulatory Compliance
- Understanding Liability Risk from Using Health Care Artificial Intelligence ToolsMichelle M. Mello; Neel Guha. 2024. New England Journal of Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2); Physician AI Liability and Regulatory Compliance (passage 3)
- Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studiesMyura Nagendran; Yang Chen; Christopher A Lovejoy; et al. 2020. BMJjournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- official guidance for software and AI as a medical deviceCited in: Physician AI Liability and Regulatory Compliance
- Risk, Accountability, and the Clinician’s Role in the Artificial Intelligence EraAndrew S. Parsons; Adam Rodman. 2026. Annals of Internal Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance
- Potential Liability for Physicians Using Artificial IntelligenceW. Nicholson Price II; Sara Gerke; I. Glenn Cohen. 2019. JAMAjournal articleCited in: Physician AI Liability and Regulatory Compliance
- Machine Learning in MedicineAlvin Rajkomar; Jeffrey Dean; Isaac Kohane. 2019. New England Journal of Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance
- A governance model for the application of AI in health careSandeep Reddy; Sonia Allan; Simon Coghlan; et al. 2020. Journal of the American Medical Informatics Associationjournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- Stop explaining black box machine learning models for high stakes decisions and use interpretable models insteadCynthia Rudin. 2019. Nature Machine Intelligencejournal articleCited in: Physician AI Liability and Regulatory Compliance
- scheduled to take effect October 1, 2026Cited in: Physician AI Liability and Regulatory Compliance
- Sendak et al., 2020Cited in: Physician AI Liability and Regulatory Compliance
- How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized TrialAlessandro Tacconelli; Jakob Merane; Aileen Nielsen; et al. 2026. Journal of Nuclear Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance
- Texas SB 1188Cited in: Physician AI Liability and Regulatory Compliance
- High-performance medicine: the convergence of human and artificial intelligenceEric J. Topol. 2019. Nature Medicinejournal articleCited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
- WHO, 2025Cited in: Physician AI Liability and Regulatory Compliance (passage 1); Physician AI Liability and Regulatory Compliance (passage 2)
Part IV: Practical Tools for Physicians
AI Tools Every Physician Should Know
References
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: AI Tools Every Physician Should Know (passage 1); AI Tools Every Physician Should Know (passage 2)
- A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-BeingMajid Afshar; Mary Ryan Baumann; Felice Resnik; et al. 2025. NEJM AIjournal articleCited in: AI Tools Every Physician Should Know
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trialAmbrose Agweyu; Paul Mwaniki; Vaishnavi Menon; et al. 2026. Nature Medicinejournal articleCited in: AI Tools Every Physician Should Know
- AI Resource HubCited in: AI Tools Every Physician Should Know
- Enhancing clinical documentation with ambient artificial intelligence: a quality improvement survey assessing clinician perspectives on work burden, burnout, and job satisfactionMichael Albrecht; Denton Shanks; Tina Shah; et al. 2025. JAMIA Openjournal articleCited in: AI Tools Every Physician Should Know
- The Use of Generative Artificial Intelligence (AI) in Academic Research: A Review of the Consensus AppOlukayode E Apata; Oi-Man Kwok; Yuan-Hsuan Lee. 2025. Cureusjournal articleCited in: AI Tools Every Physician Should Know
- OpenEvidence clinical question-answering platform: systematic review of early evaluationsYaara Artsi; Vera Sorin; Benjamin S. Glicksberg; et al. 2026. npj Digital Medicinejournal articleCited in: AI Tools Every Physician Should Know
- Bakker et al., Hypothesis 2023Cited in: AI Tools Every Physician Should Know
- DXplainG. Octo Barnett. 1987. JAMAjournal articleCited in: AI Tools Every Physician Should Know
- Beaubier et al., 2019Cited in: AI Tools Every Physician Should Know
- The Value of Automated Diabetic Retinopathy Screening with the EyeArt System: A Study of More Than 100,000 Consecutive Encounters from People with DiabetesMalavika Bhaskaranand; Chaithanya Ramachandra; Sandeep Bhat; et al. 2019. Diabetes Technology & Therapeuticsjournal articleCited in: AI Tools Every Physician Should Know
- BJR|Open, 2024Cited in: AI Tools Every Physician Should Know
- BMJ Open, 2024Cited in: AI Tools Every Physician Should Know
- Differential Diagnosis Generators: an Evaluation of Currently Available Computer ProgramsWilliam F. Bond; Linda M. Schwartz; Kevin R. Weaver; et al. 2012. Journal of General Internal Medicinejournal articleCited in: AI Tools Every Physician Should Know
- Chancellor’s announcementCited in: AI Tools Every Physician Should Know
- ChatGPT HealthCited in: AI Tools Every Physician Should Know
- Real-world performance evaluation of a commercial deep learning model for intracranial hemorrhage detectionMohammadreza Chavoshi; Aawez Mansuri; Wasif Bala; et al. 2025. npj Digital Medicinejournal articleCited in: AI Tools Every Physician Should Know
- Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope PlatformJohn S. Chorba; Avi M. Shapiro; Le Le; et al. 2021. Journal of the American Heart Associationjournal articleCited in: AI Tools Every Physician Should Know
- Claude for HealthcareCited in: AI Tools Every Physician Should Know (passage 1); AI Tools Every Physician Should Know (passage 2)
- Danad et al., 2019Cited in: AI Tools Every Physician Should Know
- Comparison of an Initial Risk-Based Testing Strategy vs Usual Testing in Stable Symptomatic Patients With Suspected Coronary Artery DiseasePamela S. Douglas; Michael G. Nanna; Michelle D. Kelsey; et al. 2023. JAMA Cardiologyjournal articleCited in: AI Tools Every Physician Should Know
- Eko Murmur Analysis Software, K213794Cited in: AI Tools Every Physician Should Know
- FDA AI-Enabled Medical DevicesCited in: AI Tools Every Physician Should Know (passage 1); AI Tools Every Physician Should Know (passage 2); AI Tools Every Physician Should Know (passage 3)
- FDA DEN180001Cited in: AI Tools Every Physician Should Know (passage 1); AI Tools Every Physician Should Know (passage 2)
- FDA, 2017Cited in: AI Tools Every Physician Should Know
- Fierce Healthcare, 2026Cited in: AI Tools Every Physician Should Know
- FierceHealthcare, 2025Cited in: AI Tools Every Physician Should Know
- Viz.ai Implementation of Stroke Augmented Intelligence and Communications Platform to Improve Indicators and Outcomes for a Comprehensive Stroke Center and NetworkM.E. Figurelle; D.M. Meyer; E.S. Perrinez; et al. 2023. American Journal of Neuroradiologyjournal articleCited in: AI Tools Every Physician Should Know
- FoundationOne Liquid CDx, P190032Cited in: AI Tools Every Physician Should Know
- Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencingGarrett M Frampton; Alex Fichtenholtz; Geoff A Otto; et al. 2013. Nature Biotechnologyjournal articleCited in: AI Tools Every Physician Should Know
- Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studiesKaroline Freeman; Jacqueline Dinnes; Naomi Chuchu; et al. 2020. BMJjournal articleCited in: AI Tools Every Physician Should Know
- How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPsStephen Gilbert; Alicia Mehl; Adel Baluch; et al. 2020. BMJ Openjournal articleCited in: AI Tools Every Physician Should Know
- Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age‐related macular degenerationCristina González‐Gonzalo; Verónica Sánchez‐Gutiérrez; Paula Hernández‐Martínez; et al. 2020. Acta Ophthalmologicajournal articleCited in: AI Tools Every Physician Should Know
- Google Health AI Developer FoundationsCited in: AI Tools Every Physician Should Know
- The impact of nuance DAX ambient listening AI documentation: a cohort studyTyler Haberle; Courtney Cleveland; Greg L Snow; et al. 2024. Journal of the American Medical Informatics Associationjournal articleCited in: AI Tools Every Physician Should Know
- Hassan et al., Gastrointestinal Endoscopy 2020Cited in: AI Tools Every Physician Should Know
- HealthBenchCited in: AI Tools Every Physician Should Know
- Cardiorespiratory instability before and after implementing an integrated monitoring system*Marilyn Hravnak; Michael A. DeVita; Amy Clontz; et al. 2011. Critical Care Medicinejournal articleCited in: AI Tools Every Physician Should Know
- Hravnak et al., 2008Cited in: AI Tools Every Physician Should Know
- Impact of Ambient Artificial Intelligence Documentation on Cognitive LoadTaina J. Hudson; Michael Albrecht; Timothy R. Smith; et al. 2025. Mayo Clinic Proceedings: Digital Healthjournal articleCited in: AI Tools Every Physician Should Know
- Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology WorkloadJulianna D. Ianni; Rajath E. Soans; Sivaramakrishnan Sankarapandian; et al. 2020. Scientific Reportsjournal articleCited in: AI Tools Every Physician Should Know
- IDx-DR v2.3, K213037Cited in: AI Tools Every Physician Should Know
- InVision Precision LVEF, K232331Cited in: AI Tools Every Physician Should Know
- Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic RetinopathyEli Ipp; David Liljenquist; Bruce Bode; et al. 2021. JAMA Network Openjournal articleCited in: AI Tools Every Physician Should Know
- Use of UpToDate and outcomes in US hospitalsThomas Isaac; Jie Zheng; Ashish Jha. 2012. Journal of Hospital Medicinejournal articleCited in: AI Tools Every Physician Should Know
- Jiang et al., 2025Cited in: AI Tools Every Physician Should Know
- Khandekar et al., 2024Cited in: AI Tools Every Physician Should Know
- Korom et al., 2025, preprintCited in: AI Tools Every Physician Should Know
- Comparison of Elicit AI and Traditional Literature Searching in Evidence Syntheses Using Four Case StudiesOscar Lau; Su Golder. 2025. Cochrane Evidence Synthesis and Methodsjournal articleCited in: AI Tools Every Physician Should Know
- Ambient AI Scribes in Clinical Practice: A Randomized TrialPaul J. Lukac; William Turner; Sitaram Vangala; et al. 2025. NEJM AIjournal articleCited in: AI Tools Every Physician Should Know
- Ambient artificial intelligence scribes: utilization and impact on documentation timeStephen P Ma. 2025. Journal of the American Medical Informatics Associationjournal articleCited in: AI Tools Every Physician Should Know
- Vision-Enabled AI scribes reduce omissions in clinical conversations: evidence from simulated medication historiesBradley D. Menz; Nicholas L. Scarfo; Natansh D. Modi; et al. 2026. npj Digital Medicinejournal articleCited in: AI Tools Every Physician Should Know
- Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic UseAkhil Narang; Richard Bae; Ha Hong; et al. 2021. JAMA Cardiologyjournal articleCited in: AI Tools Every Physician Should Know
- NCBI NBK611329Cited in: AI Tools Every Physician Should Know
- scite: A smart citation index that displays the context of citations and classifies their intent using deep learningJosh M. Nicholson; Milo Mordaunt; Patrice Lopez; et al. 2021. Quantitative Science Studiesjournal articleCited in: AI Tools Every Physician Should Know
- OpenAI et al., 2025, preprintCited in: AI Tools Every Physician Should Know
- OpenAI for HealthcareCited in: AI Tools Every Physician Should Know
- OpenAI, September 2026Cited in: AI Tools Every Physician Should Know
- OpenEvidence, March 2026Cited in: AI Tools Every Physician Should Know
- Paige Prostate, DEN200080Cited in: AI Tools Every Physician Should Know (passage 1); AI Tools Every Physician Should Know (passage 2)
- Assessing the quality of AI-generated clinical notes: validated evaluation of a large language model ambient scribeErin Palm; Astrit Manikantan; Herprit Mahal; et al. 2025. Frontiers in Artificial Intelligencejournal articleCited in: AI Tools Every Physician Should Know
- An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracySudhir Perincheri; Angelique Wolf Levi; Romulo Celli; et al. 2021. Modern Pathologyjournal articleCited in: AI Tools Every Physician Should Know
- Autonomous Chest Radiograph Reporting Using AI: Estimation of Clinical ImpactLouis L. Plesner; Felix C. Müller; Janus D. Nybing; et al. 2023. Radiologyjournal articleCited in: AI Tools Every Physician Should Know
- PubMed record, 2026Cited in: AI Tools Every Physician Should Know
- Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitisHanna Pulaski; Stephen A. Harrison; Shraddha S. Mehta; et al. 2025. Nature Medicinejournal articleCited in: AI Tools Every Physician Should Know
- AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human PerformanceDaphne Resch; Roberto Lo Gullo; Jonas Teuwen; et al. 2024. Radiology: Imaging Cancerjournal articleCited in: AI Tools Every Physician Should Know
- Ruijsink et al., 2022Cited in: AI Tools Every Physician Should Know
- External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening MammogramsMattie Salim; Erik Wåhlin; Karin Dembrower; et al. 2020. JAMA Oncologyjournal articleCited in: AI Tools Every Physician Should Know
- Large language models encode clinical knowledgeKaran Singhal; Shekoofeh Azizi; Tao Tu; et al. 2023. Naturejournal articleCited in: AI Tools Every Physician Should Know
- STAT News, February 2026Cited in: AI Tools Every Physician Should Know
- STAT News, January 2026Cited in: AI Tools Every Physician Should Know
- STAT News, July 2025Cited in: AI Tools Every Physician Should Know
- Vanderbilt University Medical Center, 2026Cited in: AI Tools Every Physician Should Know
- General-purpose large language models outperform specialized clinical AI tools on medical benchmarksKrithik Vishwanath; Anton Alyakin; Mrigayu Ghosh; et al. 2026. Nature Medicinejournal articleCited in: AI Tools Every Physician Should Know
- Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Intracranial HemorrhageAndrew F. Voter; Ece Meram; John W. Garrett; et al. 2021. Journal of the American College of Radiologyjournal articleCited in: AI Tools Every Physician Should Know
- Impact of Artificial Intelligence on Miss Rate of Colorectal NeoplasiaMichael B. Wallace; Prateek Sharma; Pradeep Bhandari; et al. 2022. Gastroenterologyjournal articleCited in: AI Tools Every Physician Should Know
- WAVE Clinical Platform, K171056Cited in: AI Tools Every Physician Should Know
- Wong et al., 2021Cited in: AI Tools Every Physician Should Know
Recommended reading
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- DXplainRecommended in: AI Tools Every Physician Should Know
- Eko AnalysisRecommended in: AI Tools Every Physician Should Know
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Large Language Models in Clinical Practice
References
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trialAmbrose Agweyu; Paul Mwaniki; Vaishnavi Menon; et al. 2026. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- AMA, February 2025Cited in: Large Language Models in Clinical Practice
- OpenEvidence clinical question-answering platform: systematic review of early evaluationsYaara Artsi; Vera Sorin; Benjamin S. Glicksberg; et al. 2026. npj Digital Medicinejournal articleCited in: Large Language Models in Clinical Practice
- A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisationElham Asgari; Nina Montaña-Brown; Magda Dubois; et al. 2025. npj Digital Medicinejournal articleCited in: Large Language Models in Clinical Practice
- Reliability of LLMs as medical assistants for the general public: a randomized preregistered studyAndrew M. Bean; Rebecca Elizabeth Payne; Guy Parsons; et al. 2026. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- Fidelity of Medical Reasoning in Large Language ModelsSuhana Bedi; Yixing Jiang; Philip Chung; et al. 2025. JAMA Network Openjournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- MedHELM: Holistic evaluation of large language models in medicineSuhana Bedi. 2026. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- Performance of a large language model on the reasoning tasks of a physicianPeter G. Brodeur; Thomas A. Buckley; Zahir Kanjee; et al. 2026. Sciencejournal articleCited in: Large Language Models in Clinical Practice
- Clinical Reasoning of a Generative Artificial Intelligence Model Compared With PhysiciansStephanie Cabral; Daniel Restrepo; Zahir Kanjee; et al. 2024. JAMA Internal Medicinejournal articleCited in: Large Language Models in Clinical Practice
- Hallucination Rates and Reference Accuracy of ChatGPT and Bard for Systematic Reviews: Comparative AnalysisMikaël Chelli; Jules Descamps; Vincent Lavoué; et al. 2024. Journal of Medical Internet Researchjournal articleCited in: Large Language Models in Clinical Practice
- DeepSeek Deployed in 90 Chinese Tertiary Hospitals: How Artificial Intelligence Is Transforming Clinical PracticeJishizhan Chen; Chunying Miao. 2025. Journal of Medical Systemsjournal articleCited in: Large Language Models in Clinical Practice
- LLM-assisted systematic review of large language models in clinical medicineSully F. Chen; Anton Alyakin; Andreas Seas; et al. 2026. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice
- Chew, 2025Cited in: Large Language Models in Clinical Practice
- Claude for HealthcareCited in: Large Language Models in Clinical Practice
- CMS Transmittal 12897Cited in: Large Language Models in Clinical Practice
- Costa-Gomes et al., 2026, Microsoft ResearchCited in: Large Language Models in Clinical Practice
- crfm.stanford.edu/helm/medhelmCited in: Large Language Models in Clinical Practice
- AI Scribe Use in Residency Training: A Call for Specialty Society Guidance in Graduate Medical EducationJulia Giordano; Elizabeth Jones. 2026. Advances in Medical Education and Practicejournal articleCited in: Large Language Models in Clinical Practice
- Automation bias: a systematic review of frequency, effect mediators, and mitigatorsKate Goddard; Abdul Roudsari; Jeremy C Wyatt. 2012. Journal of the American Medical Informatics Associationjournal articleCited in: Large Language Models in Clinical Practice
- Large Language Model Influence on Diagnostic ReasoningEthan Goh; Robert Gallo; Jason Hom; et al. 2024. JAMA Network Openjournal articleCited in: Large Language Models in Clinical Practice
- GPT-4 assistance for improvement of physician performance on patient care tasks: a randomized controlled trialEthan Goh; Robert J. Gallo; Eric Strong; et al. 2025. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- Learning to Fake It: Limited Responses and Fabricated References Provided by ChatGPT for Medical QuestionsJocelyn Gravel; Madeleine D’Amours-Gravel; Esli Osmanlliu. 2023. Mayo Clinic Proceedings: Digital Healthjournal articleCited in: Large Language Models in Clinical Practice
- P-1295. Use of Large Language Models Does Not Improve Model Performance for Antimicrobial Resistance Prediction in Community- and Hospital-Onset Gram Negative SepsisAlison M Hixon; Hanyang Liu; Michael J Durkin; et al. 2026. Open Forum Infectious Diseasesjournal articleCited in: Large Language Models in Clinical Practice
- AI can reason like a physician—what comes next?Ashley M. Hopkins; Erik Cornelisse. 2026. Sciencejournal articleCited in: Large Language Models in Clinical Practice
- The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care PhysiciansRyan T. Hurt; Christopher R. Stephenson; Elizabeth A. Gilman; et al. 2025. Journal of Primary Care & Community Healthjournal articleCited in: Large Language Models in Clinical Practice
- An evaluation framework for clinical use of large language models in patient interaction tasksShreya Johri; Jaehwan Jeong; Benjamin A. Tran; et al. 2025. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice
- Khandekar et al., 2024Cited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- Personas Shift Clinical Action Thresholds in Large Language ModelsEyal Klang; Alon Gorenstein; Mahmud Omar; et al. 2026preprintCited in: Large Language Models in Clinical Practice
- Delving into LLM-assisted writing in biomedical publications through excess vocabularyDmitry Kobak; Rita González-Márquez; Emőke-Ágnes Horvát; et al. 2025. Science Advancesjournal articleCited in: Large Language Models in Clinical Practice
- Guideline MachinesIsaac S. Kohane. 2026. NEJM AIjournal articleCited in: Large Language Models in Clinical Practice
- Validation of an AI-powered mobile application for personalizing medical note explanations: a mixed-methods evaluationNicholas Lamb. 2026. Frontiers in Digital Healthjournal articleCited in: Large Language Models in Clinical Practice
- Li et al., 2025Cited in: Large Language Models in Clinical Practice
- Towards conversational artificial intelligence for disease managementValentin Liévin; Anil Palepu; Wei-Hung Weng; et al. 2026. Naturejournal articleCited in: Large Language Models in Clinical Practice
- Ambient AI Scribes in Clinical Practice: A Randomized TrialPaul J. Lukac; William Turner; Sitaram Vangala; et al. 2025. NEJM AIjournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2); Large Language Models in Clinical Practice (passage 3)
- Maddox et al., 2025Cited in: Large Language Models in Clinical Practice
- Assessment of Large Language Models in Clinical Reasoning: A Novel Benchmarking StudyLiam G. McCoy; Rajiv Swamy; Nidhish Sagar; et al. 2025. NEJM AIjournal articleCited in: Large Language Models in Clinical Practice
- Mehandru et al., 2025, preprintCited in: Large Language Models in Clinical Practice
- Mehta, TechCrunch, September 2026Cited in: Large Language Models in Clinical Practice
- Understanding Liability Risk from Using Health Care Artificial Intelligence ToolsMichelle M. Mello; Neel Guha. 2024. New England Journal of Medicinejournal articleCited in: Large Language Models in Clinical Practice
- NEJM AI, 2025Cited in: Large Language Models in Clinical Practice
- RAG in Health Care: A Novel Framework for Improving Communication and Decision-Making by Addressing LLM LimitationsKaren Ka Yan Ng; Izuki Matsuba; Peter Chengming Zhang. 2025. NEJM AIjournal articleCited in: Large Language Models in Clinical Practice
- Large Language Models in Medicine: The Potentials and PitfallsJesutofunmi A. Omiye; Haiwen Gui; Shawheen J. Rezaei; et al. 2024. Annals of Internal Medicinejournal articleCited in: Large Language Models in Clinical Practice
- Reconciling how clinical reasoning is learned in the age of artificial intelligenceAriel Yuhan Ong; Margaret Sui; Kyra L. Rosen; et al. 2026. npj Digital Medicinejournal articleCited in: Large Language Models in Clinical Practice
- Enhancing Large Language Models for Clinical Decision Support by Incorporating Clinical Practice GuidelinesDavid Oniani; Xizhi Wu; Shyam Visweswaran; et al. 2024. 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI)conference paperCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- OpenAI for HealthcareCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- OpenAI Help CenterCited in: Large Language Models in Clinical Practice
- OpenAI, ChatGPT for CliniciansCited in: Large Language Models in Clinical Practice
- OpenAI, September 2026Cited in: Large Language Models in Clinical Practice
- OpenEvidenceCited in: Large Language Models in Clinical Practice
- OpenEvidence, 2025Cited in: Large Language Models in Clinical Practice
- Perlis et al., 2026Cited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- ChatGPT Health performance in a structured test of triage recommendationsAshwin Ramaswamy; Alvira Tyagi; Hannah Hugo; et al. 2026. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- Changes in Clinician Time Expenditure and Visit Quantity With Adoption of Artificial Intelligence–Powered ScribesLisa S. Rotenstein; A. Jay Holmgren; Robert Thombley; et al. 2026. JAMAjournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2); Large Language Models in Clinical Practice (passage 3)
- Impact of LLM assistance on physician decision-making: a multi-country randomized controlled trialNicholas Rounding; Luthfi Saiful Arif; Janine Berg; et al. 2026. npj Digital Medicinejournal articleCited in: Large Language Models in Clinical Practice
- Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicineThomas Savage; Ashwin Nayak; Robert Gallo; et al. 2024. npj Digital Medicinejournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- Lessons from deploying the ChatEHR system at Stanford MedicineNigam H. Shah; Niraj Sehgal; Euan A. Ashley; et al. 2026. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2); Large Language Models in Clinical Practice (passage 3); Large Language Models in Clinical Practice (passage 4); Large Language Models in Clinical Practice (passage 5); Large Language Models in Clinical Practice (passage 6)
- Shah et al., 2026, technical reportCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- Structured clinical reasoning prompt enhances LLM’s diagnostic capabilities in diagnosis please quiz casesYuki Sonoda; Ryo Kurokawa; Akifumi Hagiwara; et al. 2024. Japanese Journal of Radiologyjournal articleCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- State of Clinical AI ReportCited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- State of Clinical AI Report 2026Cited in: Large Language Models in Clinical Practice (passage 1); Large Language Models in Clinical Practice (passage 2)
- An LLM chatbot to facilitate primary-to-specialist care transitions: a randomized controlled trialXinge Tao; Shuya Zhou; Kai Ding; et al. 2026. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice
- Towards conversational diagnostic artificial intelligenceTao Tu; Mike Schaekermann; Anil Palepu; et al. 2025. Naturejournal articleCited in: Large Language Models in Clinical Practice
- VeriFactCited in: Large Language Models in Clinical Practice
- General-purpose large language models outperform specialized clinical AI tools on medical benchmarksKrithik Vishwanath; Anton Alyakin; Mrigayu Ghosh; et al. 2026. Nature Medicinejournal articleCited in: Large Language Models in Clinical Practice
- Fabrication and errors in the bibliographic citations generated by ChatGPTWilliam H. Walters; Esther Isabelle Wilder. 2023. Scientific Reportsjournal articleCited in: Large Language Models in Clinical Practice
- WHO, 2025Cited in: Large Language Models in Clinical Practice
- Wong et al., 2021Cited in: Large Language Models in Clinical Practice
- “Small” Large Language Models in the Hospital: Evaluation Study on Real-World Data in a Resource-Constrained SettingHe A Xu; Romain Pythoud; Christian W Thorball; et al. 2026. Journal of Medical Internet Researchjournal articleCited in: Large Language Models in Clinical Practice
- Prompt Engineering Paradigms for Medical Applications: Scoping ReviewJamil Zaghir; Marco Naguib; Mina Bjelogrlic; et al. 2024. Journal of Medical Internet Researchjournal articleCited in: Large Language Models in Clinical Practice (passage 1)Recommended in: Large Language Models in Clinical Practice (passage 2)
Recommended reading
- Prompt Engineering as an Important Emerging Skill for Medical Professionals: TutorialBertalan Meskó. 2023. Journal of Medical Internet Researchjournal articleRecommended in: Large Language Models in Clinical Practice
AI-Assisted Clinical Documentation
References
- A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-BeingMajid Afshar; Mary Ryan Baumann; Felice Resnik; et al. 2025. NEJM AIjournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2); AI-Assisted Clinical Documentation (passage 3)
- Challenges to the Reproducibility of Machine Learning Models in Health CareAndrew L. Beam; Arjun K. Manrai; Marzyeh Ghassemi. 2020. JAMAjournal articleCited in: AI-Assisted Clinical Documentation
- Psychiatric Documentation and Management in Primary Care With Artificial Intelligence Scribe UseVictor M. Castro; Thomas H. McCoy; Pilar Verhaak; et al. 2026. JAMA Psychiatryjournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2); AI-Assisted Clinical Documentation (passage 3)
- Implementing Machine Learning in Health Care — Addressing Ethical ChallengesDanton S. Char; Nigam H. Shah; David Magnus. 2018. New England Journal of Medicinejournal articleCited in: AI-Assisted Clinical Documentation
- The Death of the Consult NoteBenjamin Chin-Yee. 2026. JAMAjournal articleCited in: AI-Assisted Clinical Documentation
- CMS Transmittal 12897Cited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2)
- digital-health guidance collectionCited in: AI-Assisted Clinical Documentation
- Everson et al., 2025Cited in: AI-Assisted Clinical Documentation
- FDA CDS Guidance, January 2026Cited in: AI-Assisted Clinical Documentation
- Liability Risks of Ambient Clinical Workflows With Artificial Intelligence for Clinicians, Hospitals, and ManufacturersSara Gerke; David A. Simon; Benjamin R. Roman. 2026. JCO Oncology Practicejournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2)
- Physician-Reported Safety Outcomes of AI-Generated Hospital Course SummariesFrançois Grolleau; April S. Liang; Timothy Keyes; et al. 2026. JAMA Network Openjournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2)
- HHS Business Associates GuidanceCited in: AI-Assisted Clinical Documentation
- Ambient Artificial Intelligence Scribes and Physician Financial ProductivityA Jay Holmgren; Cynthia L. Fenton; Robert Thombley; et al. 2026. JAMA Network Openjournal articleCited in: AI-Assisted Clinical Documentation
- Key challenges for delivering clinical impact with artificial intelligenceChristopher J. Kelly; Alan Karthikesalingam; Mustafa Suleyman; et al. 2019. BMC Medicinejournal articleCited in: AI-Assisted Clinical Documentation
- Ambient AI Scribes in Clinical Practice: A Randomized TrialPaul J. Lukac; William Turner; Sitaram Vangala; et al. 2025. NEJM AIjournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2); AI-Assisted Clinical Documentation (passage 3); AI-Assisted Clinical Documentation (passage 4)
- Vision-Enabled AI scribes reduce omissions in clinical conversations: evidence from simulated medication historiesBradley D. Menz; Nicholas L. Scarfo; Natansh D. Modi; et al. 2026. npj Digital Medicinejournal articleCited in: AI-Assisted Clinical Documentation
- Accelerating ambient AI scribe enterprise-scale deployment: Cleveland Clinic’s novel approach to health system-industry partnershipAmy Merlino; Abigail Blue; Eric Boose; et al. 2026. npj Health Systemsjournal articleCited in: AI-Assisted Clinical Documentation
- Tracing the Pen: Electronic Health Records Amid the Rise of Generative AIArash A. Nargesi; Jacqueline G. You; Danielle S. Bitterman; et al. 2026. npj Digital Medicinejournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2)
- Nong & Neprash, 2026Cited in: AI-Assisted Clinical Documentation
- Ohde et al., 2026Cited in: AI-Assisted Clinical Documentation
- Use of Ambient AI Scribes to Reduce Administrative Burden and Professional BurnoutKristine D. Olson; Daniella Meeker; Matt Troup; et al. 2025. JAMA Network Openjournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2)
- Privacy in the age of medical big dataPrice WN II, Cohen IG. 2019. Nature MedicinereviewCited in: AI-Assisted Clinical Documentation
- Machine Learning in MedicineAlvin Rajkomar; Jeffrey Dean; Isaac Kohane. 2019. New England Journal of Medicinejournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2)
- Relationship Between Clerical Burden and Characteristics of the Electronic Environment With Physician Burnout and Professional SatisfactionTait D. Shanafelt; Lotte N. Dyrbye; Christine Sinsky; et al. 2016. Mayo Clinic Proceedingsjournal articleCited in: AI-Assisted Clinical Documentation
- Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 SpecialtiesChristine Sinsky; Lacey Colligan; Ling Li; et al. 2016. Annals of Internal Medicinejournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2)
- Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical DocumentationAaron A. Tierney; Gregg Gayre; Brian Hoberman; et al. 2024. NEJM Catalystjournal articleCited in: AI-Assisted Clinical Documentation
- High-performance medicine: the convergence of human and artificial intelligenceEric J. Topol. 2019. Nature Medicinejournal articleCited in: AI-Assisted Clinical Documentation (passage 1); AI-Assisted Clinical Documentation (passage 2)
Clinical Research with AI
References
- Challenges to the Reproducibility of Machine Learning Models in Health CareAndrew L. Beam; Arjun K. Manrai; Marzyeh Ghassemi. 2020. JAMAjournal articleCited in: Clinical Research with AI
- LLM-assisted systematic review of large language models in clinical medicineSully F. Chen; Anton Alyakin; Andreas Seas; et al. 2026. Nature Medicinejournal articleCited in: Clinical Research with AI (passage 1); Clinical Research with AI (passage 2)
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methodsGary S Collins; Karel G M Moons; Paula Dhiman; et al. 2024. BMJjournal articleCited in: Clinical Research with AI (passage 1); Clinical Research with AI (passage 2)
- Randomized Controlled Trials Versus Real World Evidence: Neither Magic Nor MythHans‐Georg Eichler; Francesco Pignatti; Brigitte Schwarzer‐Daum; et al. 2021. Clinical Pharmacology & Therapeuticsjournal articleCited in: Clinical Research with AI
- CONSORT-EHEALTH: Improving and Standardizing Evaluation Reports of Web-based and Mobile Health InterventionsGunther Eysenbach; CONSORT-EHEALTH Group. 2011. Journal of Medical Internet Researchjournal articleCited in: Clinical Research with AI
- The Impact of Residual and Unmeasured Confounding in Epidemiologic Studies: A Simulation StudyZ. Fewell; G. Davey Smith; J. A. C. Sterne. 2007. American Journal of Epidemiologyjournal articleCited in: Clinical Research with AI
- The TRIPOD-LLM reporting guideline for studies using large language modelsJack Gallifant; Majid Afshar; Saleem Ameen; et al. 2025. Nature Medicinejournal articleCited in: Clinical Research with AI
- A multi-agent system for automating scientific discoveryAli E. Ghareeb; Benjamin Chang; Ludovico Mitchener; et al. 2026. Naturejournal articleCited in: Clinical Research with AI (passage 1); Clinical Research with AI (passage 2)
- Accelerating scientific discovery with Co-ScientistJuraj Gottweis; Wei-Hung Weng; Alexander Daryin; et al. 2026. Naturejournal articleCited in: Clinical Research with AI (passage 1); Clinical Research with AI (passage 2)
- Guiding Principles of Good AI Practice in Drug DevelopmentCited in: Clinical Research with AI
- Large Language Models for Chatbot Health Advice StudiesBright Huo; Amy Boyle; Nana Marfo; et al. 2025. JAMA Network Openjournal articleCited in: Clinical Research with AI
- The beginning of the end for manual chart review: LLM-mediated database constructionJacob M. Knorr; Sahil Patel; Haya T. Abusafieh; et al. 2026. npj Digital Medicinejournal articleCited in: Clinical Research with AI (passage 1); Clinical Research with AI (passage 2)
- Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI ExtensionXiaoxuan Liu; Samantha Cruz Rivera; David Moher; et al. 2020. BMJjournal articleCited in: Clinical Research with AI
- Evaluating eligibility criteria of oncology trials using real-world data and AIRuishan Liu; Shemra Rizzo; Samuel Whipple; et al. 2021. Naturejournal articleCited in: Clinical Research with AI
- Matheson et al., 2026, preprintCited in: Clinical Research with AI (passage 1); Clinical Research with AI (passage 2)
- AI in Science: Early InsightsMihai Codreanu. 2026. Google / Google DeepMind / MIT FutureTech institutional snapshotCited in: Clinical Research with AI
- Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studiesMyura Nagendran; Yang Chen; Christopher A Lovejoy; et al. 2020. BMJjournal articleCited in: Clinical Research with AI (passage 1); Clinical Research with AI (passage 2); Clinical Research with AI (passage 3)
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: Clinical Research with AI
- PROBAST+AICited in: Clinical Research with AI
- Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI ExtensionSamantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; et al. 2020. BMJjournal articleCited in: Clinical Research with AI
- Artificial Intelligence and Personalized MedicineNicholas J. Schork. 2019. Cancer Treatment and Researchbook chapterCited in: Clinical Research with AI (passage 1); Clinical Research with AI (passage 2); Clinical Research with AI (passage 3); Clinical Research with AI (passage 4)
- The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligenceViknesh Sounderajah; Ahmad Guni; Xiaoxuan Liu; et al. 2025. Nature Medicinejournal articleCited in: Clinical Research with AI
- High-performance medicine: the convergence of human and artificial intelligenceEric J. Topol. 2019. Nature Medicinejournal articleCited in: Clinical Research with AI (passage 1); Clinical Research with AI (passage 2); Clinical Research with AI (passage 3); Clinical Research with AI (passage 4)
- Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AIBaptiste Vasey; Myura Nagendran; Bruce Campbell; et al. 2022. BMJjournal articleCited in: Clinical Research with AI
- A foundation model for human-AI collaboration in medical literature miningZifeng Wang; Lang Cao; Qiao Jin; et al. 2025. Nature Communicationsjournal articleCited in: Clinical Research with AI
- WHO, 2025Cited in: Clinical Research with AI
Clinical Trials and AI
References
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trialAmbrose Agweyu; Paul Mwaniki; Vaishnavi Menon; et al. 2026. Nature Medicinejournal articleCited in: Clinical Trials and AI
- Artificial intelligence in clinical trials—state of the evidence, gaps, and next stepsAntonis A. Armoundas; Constantine Tarabanis; Joseph Loscalzo. 2026. eClinicalMedicinejournal articleCited in: Clinical Trials and AI
- Real-world validation of a multimodal LLM-powered pipeline for high-accuracy clinical trial patient matchingAnatole Callies; Quentin Bodinier; Philippe Ravaud; et al. 2025. Communications Medicinejournal articleCited in: Clinical Trials and AI
- SPIRIT 2025 statement: updated guideline for protocols of randomised trialsAn-Wen Chan; Isabelle Boutron; Sally Hopewell; et al. 2025. BMJjournal articleCited in: Clinical Trials and AI
- TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial PredictionJintai Chen; Yaojun Hu; Mingchen Cai; et al. 2025. Scientific Datajournal articleCited in: Clinical Trials and AI
- FDA, 2019Cited in: Clinical Trials and AI
- FDA, 2023Cited in: Clinical Trials and AI
- FDA, 2025Cited in: Clinical Trials and AI (passage 1); Clinical Trials and AI (passage 2)
- HINT: Hierarchical interaction network for clinical-trial-outcome predictionsTianfan Fu; Kexin Huang; Cao Xiao; et al. 2022. Patternsjournal articleCited in: Clinical Trials and AI
- A large-scale database for clinical trial outcomes and featuresChufan Gao; Jathurshan Pradeepkumar; Trisha Das; et al. 2026. Nature Healthjournal articleCited in: Clinical Trials and AI (passage 1); Clinical Trials and AI (passage 2)
- A prospective pragmatic evaluation of automatic trial matching tools in a molecular tumor boardLilia Gueguen; Louise Olgiati; Clément Brutti-Mairesse; et al. 2025. npj Precision Oncologyjournal articleCited in: Clinical Trials and AI
- Guiding Principles of Good AI Practice in Drug DevelopmentCited in: Clinical Trials and AI
- CONSORT 2025 statement: updated guideline for reporting randomised trialsSally Hopewell; An-Wen Chan; Gary S Collins; et al. 2025. BMJjournal articleCited in: Clinical Trials and AI
- Matching patients to clinical trials with large language modelsQiao Jin; Zifeng Wang; Charalampos S. Floudas; et al. 2024. Nature Communicationsjournal articleCited in: Clinical Trials and AI (passage 1); Clinical Trials and AI (passage 2); Clinical Trials and AI (passage 3)
- Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI ExtensionXiaoxuan Liu; Samantha Cruz Rivera; David Moher; et al. 2020. BMJjournal articleCited in: Clinical Trials and AI
- Evaluating eligibility criteria of oncology trials using real-world data and AIRuishan Liu; Shemra Rizzo; Samuel Whipple; et al. 2021. Naturejournal articleCited in: Clinical Trials and AI
- Clinical Trial Notifications Triggered by Artificial Intelligence–Detected Cancer ProgressionTali Mazor; Karim S. Farhat; Pavel Trukhanov; et al. 2025. JAMA Network Openjournal articleCited in: Clinical Trials and AI
- Criteria2Query 3.0: Leveraging generative large language models for clinical trial eligibility query generationJimyung Park; Yilu Fang; Casey Ta; et al. 2024. Journal of Biomedical Informaticsjournal articleCited in: Clinical Trials and AI
- Credibility assessment of in silico clinical trials for medical devicesPras Pathmanathan; Kenneth Aycock; Andreu Badal; et al. 2024. PLOS Computational Biologyjournal articleCited in: Clinical Trials and AI
- Enriching Clinical Trials With Machine LearningRoy Perlis. 2026. JAMAjournal articleCited in: Clinical Trials and AI
- Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitisHanna Pulaski; Stephen A. Harrison; Shraddha S. Mehta; et al. 2025. Nature Medicinejournal articleCited in: Clinical Trials and AI
- Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI ExtensionSamantha Cruz Rivera; Xiaoxuan Liu; An-Wen Chan; et al. 2020. BMJjournal articleCited in: Clinical Trials and AI
- A large language model for clinical outcome adjudication from telephone follow-up interviews: a secondary analysis of a multicenter randomized clinical trialZhao Shi; Bingqian Wu; Bin Hu; et al. 2025. Nature Communicationsjournal articleCited in: Clinical Trials and AI
- Sociodemographic bias in large language model clinical trial screeningShelly Soffer; Mahmud Omar; Orly Efros; et al. 2026. Journal of the American Medical Informatics Associationjournal articleCited in: Clinical Trials and AI
- Translating evidence into practice: adapting TrialGPT for real-world clinical trial eligibility screeningMahanazuddin Syed; Muayad Hamidi; Manju Bikkanuri; et al. 2026. Journal of the American Medical Informatics Associationjournal articleCited in: Clinical Trials and AI
- Teslo, 2026Cited in: Clinical Trials and AI
- Manual vs AI-Assisted Prescreening for Trial Eligibility Using Large Language Models—A Randomized Clinical TrialOzan Unlu; Matthew Varugheese; Jiyeon Shin; et al. 2025. JAMAjournal articleCited in: Clinical Trials and AI
- Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AIBaptiste Vasey; Myura Nagendran; Bruce Campbell; et al. 2022. BMJjournal articleCited in: Clinical Trials and AI
Recommended reading
- codeRecommended in: Clinical Trials and AI
- datasetRecommended in: Clinical Trials and AI
Part V: The Future of AI in Medicine
Emerging AI Technologies in Healthcare
References
- Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomised controlled trialWilliam T Abraham; Philip B Adamson; Robert C Bourge; et al. 2011. The Lancetjournal articleCited in: Emerging AI Technologies in Healthcare
- Adam et al., 2026, preprintCited in: Emerging AI Technologies in Healthcare
- AHCA/NCAL, 2026Cited in: Emerging AI Technologies in Healthcare
- Artificial Hallucinations in ChatGPT: Implications in Scientific WritingHussam Alkaissi; Samy I McFarlane. 2023. Cureusjournal articleCited in: Emerging AI Technologies in Healthcare
- Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media ForumJohn W. Ayers; Adam Poliak; Mark Dredze; et al. 2023. JAMA Internal Medicinejournal articleCited in: Emerging AI Technologies in Healthcare
- Fidelity of Medical Reasoning in Large Language ModelsSuhana Bedi; Yixing Jiang; Philip Chung; et al. 2025. JAMA Network Openjournal articleCited in: Emerging AI Technologies in Healthcare
- A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic RetinopathyEmma Beede; Elizabeth Baylor; Fred Hersch; et al. 2020. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systemsconference paperCited in: Emerging AI Technologies in Healthcare
- High Rates of Fabricated and Inaccurate References in ChatGPT-Generated Medical ContentMehul Bhattacharyya; Valerie M Miller; Debjani Bhattacharyya; et al. 2023. Cureusjournal articleCited in: Emerging AI Technologies in Healthcare
- BLS Occupational Outlook Handbook, 2025Cited in: Emerging AI Technologies in Healthcare
- A vision–language foundation model for the generation of realistic chest X-ray imagesChristian Bluethgen; Pierre Chambon; Jean-Benoit Delbrouck; et al. 2024. Nature Biomedical Engineeringjournal articleCited in: Emerging AI Technologies in Healthcare
- Brodeur et al., preprint, 2026Cited in: Emerging AI Technologies in Healthcare
- Caption Guidance, DEN190040Cited in: Emerging AI Technologies in Healthcare
- ChatGPT HealthCited in: Emerging AI Technologies in Healthcare
- Pan-cancer integrative histology-genomic analysis via multimodal deep learningRichard J. Chen; Ming Y. Lu; Drew F.K. Williamson; et al. 2022. Cancer Celljournal articleCited in: Emerging AI Technologies in Healthcare (passage 1); Emerging AI Technologies in Healthcare (passage 2)
- Humanoid Robot–Assisted Support for Health Care in Older Adults: Systematic Scoping ReviewLei Cui; Yufei Li; Xinyao Yang; et al. 2026. JMIR Agingjournal articleCited in: Emerging AI Technologies in Healthcare
- Social Robots in Elderly Care: A Systematic Review of the Effectiveness on Formal and Informal CaregiversBolin Fan; Vivian Weiqun Lou; Yuting Huang. 2026. International Journal of Social Roboticsjournal articleCited in: Emerging AI Technologies in Healthcare
- FDA AI-Enabled Medical DevicesCited in: Emerging AI Technologies in Healthcare
- Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncologyDyke Ferber; Omar S. M. El Nahhas; Georg Wölflein; et al. 2025. Nature Cancerjournal articleCited in: Emerging AI Technologies in Healthcare
- A multi-agent system for automating scientific discoveryAli E. Ghareeb; Benjamin Chang; Ludovico Mitchener; et al. 2026. Naturejournal articleCited in: Emerging AI Technologies in Healthcare
- Accuracy of Optical Heart Rate Measurements for 10 Commercial Wearables in Different Climate Conditions and Activities: Instrument Validation StudyJasper Gielen; Catharina Nina Van Oost; Glen Debard; et al. 2026. JMIR Formative Researchjournal articleCited in: Emerging AI Technologies in Healthcare
- Gomez et al., 2024Cited in: Emerging AI Technologies in Healthcare
- Google Research, January 2026Cited in: Emerging AI Technologies in Healthcare
- Accelerating scientific discovery with Co-ScientistJuraj Gottweis; Wei-Hung Weng; Alexander Daryin; et al. 2026. Naturejournal articleCited in: Emerging AI Technologies in Healthcare
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsVarun Gulshan; Lily Peng; Marc Coram; et al. 2016. JAMAjournal articleCited in: Emerging AI Technologies in Healthcare
- HealthBenchCited in: Emerging AI Technologies in Healthcare
- ChatGPT-Generated Differential Diagnosis Lists for Complex Case–Derived Clinical Vignettes: Diagnostic Accuracy EvaluationTakanobu Hirosawa; Ren Kawamura; Yukinori Harada; et al. 2023. JMIR Medical Informaticsjournal articleCited in: Emerging AI Technologies in Healthcare
- Valuing the Invaluable 2026Ari Houser; Selena Caldera; Brendan Flinn; et al. 2026reportCited in: Emerging AI Technologies in Healthcare
- K252970Cited in: Emerging AI Technologies in Healthcare (passage 1); Emerging AI Technologies in Healthcare (passage 2)
- LeCun, 2022Cited in: Emerging AI Technologies in Healthcare
- Care Robots for Community-Dwelling Older Adults: An Integrative ReviewJisan Lee; Hyeongsuk Lee; Mona Choi; et al. 2025. Healthcare Informatics Researchjournal articleCited in: Emerging AI Technologies in Healthcare
- Lindenmeyer et al., 2025Cited in: Emerging AI Technologies in Healthcare
- Human-AI teaming in healthcare: 1 + 1 > 2?Peng Liu; Jiaxin Zhang; Shuaiqi Chen; et al. 2025. npj Artificial Intelligencejournal articleCited in: Emerging AI Technologies in Healthcare (passage 1); Emerging AI Technologies in Healthcare (passage 2); Emerging AI Technologies in Healthcare (passage 3)
- Ambient AI Scribes in Clinical Practice: A Randomized TrialPaul J. Lukac; William Turner; Sitaram Vangala; et al. 2025. NEJM AIjournal articleCited in: Emerging AI Technologies in Healthcare
- Ambient artificial intelligence scribes: utilization and impact on documentation timeStephen P Ma. 2025. Journal of the American Medical Informatics Associationjournal articleCited in: Emerging AI Technologies in Healthcare
- Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict StudyAshley E. Mason; Frederick M. Hecht; Shakti K. Davis; et al. 2022. Scientific Reportsjournal articleCited in: Emerging AI Technologies in Healthcare
- American College of Chest Physicians/La Société de Réanimation de Langue Française Statement on Competence in Critical Care UltrasonographyPaul H. Mayo; Yannick Beaulieu; Peter Doelken; et al. 2009. Chestjournal articleCited in: Emerging AI Technologies in Healthcare
- Towards accurate differential diagnosis with large language modelsDaniel McDuff; Mike Schaekermann; Tao Tu; et al. 2025. Naturejournal articleCited in: Emerging AI Technologies in Healthcare
- MedASRCited in: Emerging AI Technologies in Healthcare
- Vision-Enabled AI scribes reduce omissions in clinical conversations: evidence from simulated medication historiesBradley D. Menz; Nicholas L. Scarfo; Natansh D. Modi; et al. 2026. npj Digital Medicinejournal articleCited in: Emerging AI Technologies in Healthcare
- Foundation models for generalist medical artificial intelligenceMichael Moor; Oishi Banerjee; Zahra Shakeri Hossein Abad; et al. 2023. Naturejournal articleCited in: Emerging AI Technologies in Healthcare
- Utility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic UseAkhil Narang; Richard Bae; Ha Hong; et al. 2021. JAMA Cardiologyjournal articleCited in: Emerging AI Technologies in Healthcare (passage 1); Emerging AI Technologies in Healthcare (passage 2)
- NIST SP 800-226Cited in: Emerging AI Technologies in Healthcare
- Nori et al., 2023, preprintCited in: Emerging AI Technologies in Healthcare
- Diagnostic Performance of Noninvasive Fractional Flow Reserve Derived From Coronary Computed Tomography Angiography in Suspected Coronary Artery DiseaseBjarne L. Nørgaard; Jonathon Leipsic; Sara Gaur; et al. 2014. Journal of the American College of Cardiologyjournal articleCited in: Emerging AI Technologies in Healthcare
- Patel et al., JACC Cardiovasc Imaging, 2019Cited in: Emerging AI Technologies in Healthcare
- Effect of Genotype-Guided Oral P2Y12 Inhibitor Selection vs Conventional Clopidogrel Therapy on Ischemic Outcomes After Percutaneous Coronary InterventionNaveen L. Pereira; Michael E. Farkouh; Derek So; et al. 2020. JAMAjournal articleCited in: Emerging AI Technologies in Healthcare
- Large-Scale Assessment of a Smartwatch to Identify Atrial FibrillationMarco V. Perez; Kenneth W. Mahaffey; Haley Hedlin; et al. 2019. New England Journal of Medicinejournal articleCited in: Emerging AI Technologies in Healthcare (passage 1); Emerging AI Technologies in Healthcare (passage 2)
- Early adverse physiological event detection using commercial wearables: challenges and opportunitiesJesse Phipps; Bryant Passage; Kaan Sel; et al. 2024. npj Digital Medicinejournal articleCited in: Emerging AI Technologies in Healthcare
- How to DP-fy ML: A Practical Guide to Machine Learning with Differential PrivacyNatalia Ponomareva; Hussein Hazimeh; Alex Kurakin; et al. 2023. Journal of Artificial Intelligence Researchjournal articleCited in: Emerging AI Technologies in Healthcare
- Autonomous agentic artificial intelligence systems in health care: friend or foe?Yiming Qin; Dian Zeng; Weizhi Ma; et al. 2026. The Lancet Digital Healthjournal articleCited in: Emerging AI Technologies in Healthcare
- Generalist biological artificial intelligence in modeling the language of lifeVishwanatha M. Rao; Serena Zhang; Brian S. Plosky; et al. 2026. Nature Biotechnologyjournal articleCited in: Emerging AI Technologies in Healthcare
- The future of digital health with federated learningNicola Rieke; Jonny Hancox; Wenqi Li; et al. 2020. npj Digital Medicinejournal articleCited in: Emerging AI Technologies in Healthcare
- Roth et al., 2020Cited in: Emerging AI Technologies in Healthcare
- Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort studyPaisan Ruamviboonsuk; Richa Tiwari; Rory Sayres; et al. 2022. The Lancet Digital Healthjournal articleCited in: Emerging AI Technologies in Healthcare
- Advancing conversational diagnostic AI with multimodal reasoningKhaled Saab; Chunjong Park; Tim Strother; et al. 2026. Nature Medicinejournal articleCited in: Emerging AI Technologies in Healthcare
- From Concept to Clinic: Real World Evidence for Autonomous AI Deployment in Primary Care TelemedicineAgustina Saenz; Elliot Schumacher; Dhruv Naik; et al. 2026preprintCited in: Emerging AI Technologies in Healthcare
- Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burdenShreya J Shah. 2025. Journal of the American Medical Informatics Associationjournal articleCited in: Emerging AI Technologies in Healthcare
- Longitudinal Study Evaluating the Association Between Physician Burnout and Changes in Professional Work EffortTait D. Shanafelt; Michelle Mungo; Jaime Schmitgen; et al. 2016. Mayo Clinic Proceedingsjournal articleCited in: Emerging AI Technologies in Healthcare
- Large language models encode clinical knowledgeKaran Singhal; Shekoofeh Azizi; Tao Tu; et al. 2023. Naturejournal articleCited in: Emerging AI Technologies in Healthcare (passage 1); Emerging AI Technologies in Healthcare (passage 2)
- Toward expert-level medical question answering with large language modelsKaran Singhal; Tao Tu; Juraj Gottweis; et al. 2025. Nature Medicinejournal articleCited in: Emerging AI Technologies in Healthcare (passage 1); Emerging AI Technologies in Healthcare (passage 2); Emerging AI Technologies in Healthcare (passage 3)
- Language models are an effective representation learning technique for electronic health record dataEthan Steinberg; Ken Jung; Jason A. Fries; et al. 2021. Journal of Biomedical Informaticsjournal articleCited in: Emerging AI Technologies in Healthcare
- Accuracy of commercially available smartphone applications for the detection of melanomaM.D. Sun; J. Kentley; P. Mehta; et al. 2022. British Journal of Dermatologyjournal articleCited in: Emerging AI Technologies in Healthcare
- Effectiveness of Al-Assisted Patient Health Education Using Voice Cloning and ChatGPT: Prospective Randomized Controlled TrialYan Sun; Shangqing Xu; Hongying Jin; et al. 2026. Journal of Medical Internet Researchjournal articleCited in: Emerging AI Technologies in Healthcare
- A multimodal sleep foundation model for disease predictionRahul Thapa; Magnus Ruud Kjaer; Bryan He; et al. 2026. Nature Medicinejournal articleCited in: Emerging AI Technologies in Healthcare
- Robots in Assisted Living Facilities: Scoping ReviewKatie Trainum; Rachel Tunis; Bo Xie; et al. 2023. JMIR Agingjournal articleCited in: Emerging AI Technologies in Healthcare
- Artificial intelligence agents in cancer research and oncologyDaniel Truhn; Shekoofeh Azizi; James Zou; et al. 2026. Nature Reviews Cancerjournal articleCited in: Emerging AI Technologies in Healthcare
- TwolabsCited in: Emerging AI Technologies in Healthcare
- van Steijn et al., 2026Cited in: Emerging AI Technologies in Healthcare
- Sleep and temperature data from wearable devices support noninvasive detection of diabetes mellitus in a large-scale, retrospective analysisVarun K. Viswanath; Shreenithi Navaneethan; Jamison H. Burks; et al. 2026. Communications Medicinejournal articleCited in: Emerging AI Technologies in Healthcare
- WHO, 2025Cited in: Emerging AI Technologies in Healthcare
- Wornow et al., 2023Cited in: Emerging AI Technologies in Healthcare (passage 1); Emerging AI Technologies in Healthcare (passage 2)
- Y CombinatorCited in: Emerging AI Technologies in Healthcare
- Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis DiagnosisShao Zhang; Jianing Yu; Xuhai Xu; et al. 2024. Proceedings of the CHI Conference on Human Factors in Computing Systemsconference paperCited in: Emerging AI Technologies in Healthcare
- Zhang et al., 2023Cited in: Emerging AI Technologies in Healthcare
Recommended reading
- Hugging FaceRecommended in: Emerging AI Technologies in Healthcare
- Hugging FaceRecommended in: Emerging AI Technologies in Healthcare
AI and Global Health Equity
References
- Safety of a large language model-based clinical decision support system in African primary healthcareAmbrose Agweyu; Paul Mwaniki; Wilkister Musau; et al. 2026. Nature Healthjournal articleCited in: AI and Global Health Equity
- Anthropic, 2026Cited in: AI and Global Health Equity
- Barron et al., 2018Cited in: AI and Global Health Equity
- Global physician perspectives on artificial intelligence in healthcare across 50 countries and territoriesBayarbaatar Bold; Oguzhan Serin; Lukitaningrum Tantri Adhiatma; et al. 2026. npj Digital Medicinejournal articleCited in: AI and Global Health Equity
- Chen et al., 2025Cited in: AI and Global Health Equity
- Gates Foundation, 2024-2026Cited in: AI and Global Health Equity
- India CodeCited in: AI and Global Health Equity
- ITU, 2025Cited in: AI and Global Health Equity (passage 1); AI and Global Health Equity (passage 2); AI and Global Health Equity (passage 3)
- Lancet Global Health, ongoingCited in: AI and Global Health Equity
- LeFevre et al., 2018Cited in: AI and Global Health Equity
- Li et al., Nature Medicine, 2024Cited in: AI and Global Health Equity
- AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear imagesRuicun Liu; Tuoyu Liu; Tingting Dan; et al. 2023. Patternsjournal articleCited in: AI and Global Health Equity
- Marey et al., 2025Cited in: AI and Global Health Equity (passage 1); AI and Global Health Equity (passage 2); AI and Global Health Equity (passage 3)
- Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health RadiologyDaniel J. Mollura; Melissa P. Culp; Erica Pollack; et al. 2020. Radiologyjournal articleCited in: AI and Global Health Equity
- Olatunji et al., ACL 2025Cited in: AI and Global Health Equity
- Ong et al., 2026Cited in: AI and Global Health Equity (passage 1); AI and Global Health Equity (passage 2)
- Large language model diagnostic assistance for physicians in a lower-middle-income country: a randomized controlled trialIhsan Ayyub Qazi; Ayesha Ali; Asad Ullah Khawaja; et al. 2026. Nature Healthjournal articleCited in: AI and Global Health Equity
- Impact of LLM assistance on physician decision-making: a multi-country randomized controlled trialNicholas Rounding; Luthfi Saiful Arif; Janine Berg; et al. 2026. npj Digital Medicinejournal articleCited in: AI and Global Health Equity
- Racial Bias in Pulse Oximetry MeasurementMichael W. Sjoding; Robert P. Dickson; Theodore J. Iwashyna; et al. 2020. New England Journal of Medicinejournal articleCited in: AI and Global Health Equity
- South African National Department of HealthCited in: AI and Global Health Equity
- South African National Department of HealthCited in: AI and Global Health Equity (passage 1); AI and Global Health Equity (passage 2)
- WHO, 2021Cited in: AI and Global Health Equity
- WHO, 2025Cited in: AI and Global Health Equity
- WHO/ITU/WIPO, 2023Cited in: AI and Global Health Equity
- Wong et al., 2021Cited in: AI and Global Health Equity
- World Bank, 2026Cited in: AI and Global Health Equity (passage 1); AI and Global Health Equity (passage 2)
- Zeng et al., JAMA, 2025Cited in: AI and Global Health Equity
Recommended reading
- afrimedqa.comRecommended in: AI and Global Health Equity
Healthcare Policy and AI Governance
References
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: Healthcare Policy and AI Governance
- ACR-SIIM Practice Parameter for Imaging AICited in: Healthcare Policy and AI Governance
- HITECH Act Drove Large Gains In Hospital Electronic Health Record AdoptionJulia Adler-Milstein; Ashish K. Jha. 2017. Health Affairsjournal articleCited in: Healthcare Policy and AI Governance
- AHA News, 2017Cited in: Healthcare Policy and AI Governance
- Machine Learning-Enabled Medical Devices Authorized by the US Food and Drug Administration in 2024: Regulatory Characteristics, Predicate Lineage, and Transparency ReportingBassel Almarie; Luis Fernando Gonzalez-Gonzalez; Lucas Antônio dos Santos Barbosa; et al. 2025. Biomedicinesjournal articleCited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- AMA AI Principles, 2018Cited in: Healthcare Policy and AI Governance
- AMA, 2024Cited in: Healthcare Policy and AI Governance
- AMA, 2025Cited in: Healthcare Policy and AI Governance
- America’s AI Action PlanCited in: Healthcare Policy and AI Governance
- Arizona H.B. 2177Cited in: Healthcare Policy and AI Governance
- Medicaid Work Requirements—The Physician as ArbiterHenry Bair. 2026. JAMA Internal Medicinejournal articleCited in: Healthcare Policy and AI Governance
- Becker’s Hospital Review, January 2026Cited in: Healthcare Policy and AI Governance
- A Licensure Framework for Autonomous Clinical AIAlon Bergman. 2026. JAMAjournal articleCited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- Brynjolfsson et al., AEJ: Macroeconomics, 2021Cited in: Healthcare Policy and AI Governance
- CHAI RAIGCited in: Healthcare Policy and AI Governance
- CHAI Use CasesCited in: Healthcare Policy and AI Governance
- CHAI, 2026Cited in: Healthcare Policy and AI Governance
- Citizen Petition, October 2025Cited in: Healthcare Policy and AI Governance
- Clemens & Gottlieb, J Polit Econ, 2017Cited in: Healthcare Policy and AI Governance
- CMS 2026 PFS datasetCited in: Healthcare Policy and AI Governance
- CMS ACCESS ModelCited in: Healthcare Policy and AI Governance
- CMS CY 2026 Physician Fee ScheduleCited in: Healthcare Policy and AI Governance
- CMS EHR overviewCited in: Healthcare Policy and AI Governance
- CMS enrollment dataCited in: Healthcare Policy and AI Governance
- CMS, 2020Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2); Healthcare Policy and AI Governance (passage 3)
- Congress.gov, H.R.3288Cited in: Healthcare Policy and AI Governance
- Congress.gov, S.2750Cited in: Healthcare Policy and AI Governance
- Cutler et al., BMJ Open, 2018Cited in: Healthcare Policy and AI Governance
- Delaware H.J.R. 7Cited in: Healthcare Policy and AI Governance
- European Commission AI ActCited in: Healthcare Policy and AI Governance
- FDA AI-Enabled Medical DevicesCited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- FDA CDS FAQsCited in: Healthcare Policy and AI Governance
- FDA CDS Guidance, January 2026Cited in: Healthcare Policy and AI Governance
- FDA DEN180001Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- FDA Final Response, April 2026Cited in: Healthcare Policy and AI Governance
- FDA General Wellness Guidance, 2026Cited in: Healthcare Policy and AI Governance
- FDA MAUDE limitationsCited in: Healthcare Policy and AI Governance
- FDA PCCP guidanceCited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- FDA QMSRCited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- FDA TEMPO FAQCited in: Healthcare Policy and AI Governance
- FDA, 2026Cited in: Healthcare Policy and AI Governance
- FDA, 2026Cited in: Healthcare Policy and AI Governance
- FDA, August 2026Cited in: Healthcare Policy and AI Governance
- FDA, July 2026Cited in: Healthcare Policy and AI Governance
- FDA-2026-N-7874Cited in: Healthcare Policy and AI Governance
- Federal Register, 2025Cited in: Healthcare Policy and AI Governance
- Federal Register, 2025Cited in: Healthcare Policy and AI Governance
- Federal Register, 2026Cited in: Healthcare Policy and AI Governance
- Federal Register, December 2025Cited in: Healthcare Policy and AI Governance
- Ganguli et al., JAMA, 2015Cited in: Healthcare Policy and AI Governance
- Expert perspectives on recent US digital medicine regulation policy changes and the TEMPO pilotStephen Gilbert; Tinglong Dai. 2026. npj Digital Medicinejournal articleCited in: Healthcare Policy and AI Governance
- The Missing Dimension in Clinical AI: Making Hidden Values VisibleCarey Goldberg; Ran D. Balicer; Mamatha Bhat; et al. 2026. NEJM AIjournal articleCited in: Healthcare Policy and AI Governance
- Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart AssociationKate Hanneman; David Playford; Damini Dey; et al. 2024. Circulationjournal articleCited in: Healthcare Policy and AI Governance
- HHS Telehealth Policy, 2026Cited in: Healthcare Policy and AI Governance
- https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781307Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligenceCited in: Healthcare Policy and AI Governance
- Interpretive Diagnostic Error Reduction guidelineCited in: Healthcare Policy and AI Governance
- IOM, 2012Cited in: Healthcare Policy and AI Governance
- ISO, 2023Cited in: Healthcare Policy and AI Governance
- Pragmatic Approaches to the Evaluation and Monitoring of Artificial Intelligence in Health Care: A Science Advisory From the American Heart AssociationSneha S. Jain; Shinichi Goto; Jennifer L. Hall; et al. 2025. Circulationjournal articleCited in: Healthcare Policy and AI Governance
- Joint Commission and CHAI, September 2025Cited in: Healthcare Policy and AI Governance
- Joint Commission, 2026Cited in: Healthcare Policy and AI Governance
- The absence of full lifecycle risk management for AI-based medical devices in radiologyJia Li; Zicong Guo; Yi Guo; et al. 2026. npj Digital Medicinejournal articleCited in: Healthcare Policy and AI Governance
- Medical-device standards systemCited in: Healthcare Policy and AI Governance
- Evaluating transparency in AI/ML model characteristics for FDA-reviewed medical devicesViraj Mehta; Abhinav Komanduri; Rishabh Singh Bhadouriya; et al. 2025. npj Digital Medicinejournal articleCited in: Healthcare Policy and AI Governance
- Understanding Liability Risk from Using Health Care Artificial Intelligence ToolsMichelle M. Mello; Neel Guha. 2024. New England Journal of Medicinejournal articleCited in: Healthcare Policy and AI Governance
- Roles for AI in Implementation of Medicaid Work RequirementsMichelle M. Mello; Himaja Nagireddy. 2026. JAMA Health Forumjournal articleCited in: Healthcare Policy and AI Governance
- Current Use And Evaluation Of Artificial Intelligence And Predictive Models In US HospitalsPaige Nong; Julia Adler-Milstein; Nate C. Apathy; et al. 2025. Health Affairsjournal articleCited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- Optum, 2017Cited in: Healthcare Policy and AI Governance
- The epidemiology of artificial intelligenceHarsh Parikh; Tyler McCormick; Emily K. Johnson; et al. 2026. Nature Healthjournal articleCited in: Healthcare Policy and AI Governance
- PDFCited in: Healthcare Policy and AI Governance
- PDFCited in: Healthcare Policy and AI Governance
- Premarket guidance for machine-learning-enabled medical devicesCited in: Healthcare Policy and AI Governance
- press announcementCited in: Healthcare Policy and AI Governance
- Regulation (EU) 2024/1689Cited in: Healthcare Policy and AI Governance
- Regulation (EU) 2026/1744Cited in: Healthcare Policy and AI Governance
- Rock Health, 2025Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- Sahni et al., McKinsey, 2021Cited in: Healthcare Policy and AI Governance
- Sahni et al., NBER, 2023Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- scheduled to take effect October 1, 2026Cited in: Healthcare Policy and AI Governance
- Adoption and use of generative AI at an academic medical centerShreya Shah. 2026. Preprint/Internal ReportCited in: Healthcare Policy and AI Governance
- Silicon Valley Bank, 2026Cited in: Healthcare Policy and AI Governance
- Software and AI as a Medical Device Change ProgrammeCited in: Healthcare Policy and AI Governance
- Software as a Medical Device review resourcesCited in: Healthcare Policy and AI Governance
- Tabassi, 2023Cited in: Healthcare Policy and AI Governance
- Texas HB 149 / TRAIGACited in: Healthcare Policy and AI Governance
- A scoping review of silent trials for medical artificial intelligenceLana Tikhomirov; Carolyn Semmler; Noah Prizant; et al. 2026. Nature Healthjournal articleCited in: Healthcare Policy and AI Governance
- Utah Office of AI Policy, 2026Cited in: Healthcare Policy and AI Governance
- White House, 2025Cited in: Healthcare Policy and AI Governance
- WHO, 2021Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2); Healthcare Policy and AI Governance (passage 3)
- WHO, 2025Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- WHO, May 2025Cited in: Healthcare Policy and AI Governance
- Leveraging AI to reduce operational healthcare costs: lessons from other industriesAndrew Wong; Brahmajee K. Nallamothu; Christopher A. Longhurst; et al. 2026. npj Health Systemsjournal articleCited in: Healthcare Policy and AI Governance
- Wong et al., 2021Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- Wu et al., NEJM AI, 2024Cited in: Healthcare Policy and AI Governance (passage 1); Healthcare Policy and AI Governance (passage 2)
- Wyoming Legislature, 2025Cited in: Healthcare Policy and AI Governance
- Ötleş et al., 2026Cited in: Healthcare Policy and AI Governance
Medical Misinformation and AI
References
- Medical large language models are vulnerable to data-poisoning attacksDaniel Alexander Alber; Zihao Yang; Anton Alyakin; et al. 2025. Nature Medicinejournal articleCited in: Medical Misinformation and AI
- Announcements Versus Conversations to Improve HPV Vaccination Coverage: A Randomized TrialNoel T. Brewer; Megan E. Hall; Teri L. Malo; et al. 2017. Pediatricsjournal articleCited in: Medical Misinformation and AI (passage 1); Medical Misinformation and AI (passage 2)
- Brown University School of Public Health, 2022Cited in: Medical Misinformation and AI
- The FDA-approved drug ivermectin inhibits the replication of SARS-CoV-2 in vitroLeon Caly; Julian D. Druce; Mike G. Catton; et al. 2020. Antiviral Researchjournal articleCited in: Medical Misinformation and AI
- CDC, 2021Cited in: Medical Misinformation and AI
- CDC’s HPV vaccine safety informationCited in: Medical Misinformation and AI
- The emerging landscape of performance-enhancing peptides modulating GH-IGF1 axis: bridging the gap between clinical evidence and patient self-administrationAleksander Dominikowski; Zofia Rękoś; Michał Olejarz; et al. 2026. Frontiers in Endocrinologyjournal articleCited in: Medical Misinformation and AI
- The psychological drivers of misinformation belief and its resistance to correctionUllrich K. H. Ecker; Stephan Lewandowsky; John Cook; et al. 2022. Nature Reviews Psychologyjournal articleCited in: Medical Misinformation and AI
- Edelson et al., 2021Cited in: Medical Misinformation and AI (passage 1); Medical Misinformation and AI (passage 2)
- Impurity profiling of the most frequently encountered falsified polypeptide drugs on the Belgian marketSteven Janvier; Karlien Cheyns; Michaël Canfyn; et al. 2018. Talantajournal articleCited in: Medical Misinformation and AI
- Meta, January 2025Cited in: Medical Misinformation and AI
- Mapping the susceptibility of large language models to medical misinformation across clinical notes and social media: a cross-sectional benchmarking analysisMahmud Omar; Vera Sorin; Lothar H Wieler; et al. 2026. The Lancet Digital Healthjournal articleCited in: Medical Misinformation and AI (passage 1); Medical Misinformation and AI (passage 2)
- Trust in Physicians and Hospitals During the COVID-19 Pandemic in a 50-State Survey of US AdultsRoy H. Perlis; Katherine Ognyanova; Ata Uslu; et al. 2024. JAMA Network Openjournal articleCited in: Medical Misinformation and AI
- Reis et al., 2022Cited in: Medical Misinformation and AI
- A signal detection theory meta-analysis of psychological inoculation against misinformationAlmog Simchon; Tomer Zipori; Louis Teitelbaum; et al. 2026. Current Opinion in Psychologyjournal articleCited in: Medical Misinformation and AI
- The Rise of Deepfake Medical Imaging: Radiologists’ Diagnostic Accuracy in Detecting ChatGPT-generated RadiographsMickael Tordjman; Murat Yuce; Amine Ammar; et al. 2026. Radiologyjournal articleCited in: Medical Misinformation and AI
- Misinformation: susceptibility, spread, and interventions to immunize the publicSander van der Linden. 2022. Nature Medicinejournal articleCited in: Medical Misinformation and AI (passage 1); Medical Misinformation and AI (passage 2)
AI in Medical Education
References
- AAMC, 2025Cited in: AI in Medical Education
- Educational Strategies for Clinical Supervision of Artificial Intelligence UseRaja-Elie E. Abdulnour; Brian Gin; Christy K. Boscardin. 2025. New England Journal of Medicinejournal articleCited in: AI in Medical Education (passage 1); AI in Medical Education (passage 2)
- ACGME, 2025Cited in: AI in Medical Education
- AMA PolicyFinder, 2025Cited in: AI in Medical Education
- OpenEvidence clinical question-answering platform: systematic review of early evaluationsYaara Artsi; Vera Sorin; Benjamin S. Glicksberg; et al. 2026. npj Digital Medicinejournal articleCited in: AI in Medical Education
- Generative AI without guardrails can harm learning: Evidence from high school mathematicsHamsa Bastani. 2025. Proceedings of the National Academy of Sciencesjournal articleCited in: AI in Medical Education
- Macy Foundation Innovation Report Part I: Current Landscape of Artificial Intelligence in Medical EducationChristy K. Boscardin; Raja-Elie E. Abdulnour; Brian C. Gin. 2025. Academic Medicinejournal articleCited in: AI in Medical Education (passage 1)Recommended in: AI in Medical Education (passage 2)
- Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational studyKrzysztof Budzyń; Marcin Romańczyk; Diana Kitala; et al. 2025. The Lancet Gastroenterology & Hepatologyjournal articleCited in: AI in Medical Education
- Caliskan et al., 2022Cited in: AI in Medical Education (passage 1)Recommended in: AI in Medical Education (passage 2)
- Is AI making us stupid?Trent N. Cash. 2026. Trends in Cognitive Sciencesjournal articleCited in: AI in Medical Education
- FDA AI-Enabled Medical DevicesCited in: AI in Medical Education
- AI Scribe Use in Residency Training: A Call for Specialty Society Guidance in Graduate Medical EducationJulia Giordano; Elizabeth Jones. 2026. Advances in Medical Education and Practicejournal articleCited in: AI in Medical Education
- A scoping review of artificial intelligence in medical education: BEME Guide No. 84Morris Gordon; Michelle Daniel; Aderonke Ajiboye; et al. 2024. Medical Teacherjournal articleCited in: AI in Medical Education (passage 1)Recommended in: AI in Medical Education (passage 2)
- https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781307Cited in: AI in Medical Education
- What the AI era doctor should know: a scoping review of proposed artificial intelligence competencies for medical educationVictor M. Hunt; Laurine K. Sprehe; Weston C. de Lomba; et al. 2026. npj Digital Medicinejournal articleCited in: AI in Medical Education (passage 1)Recommended in: AI in Medical Education (passage 2)
- The Use of an Artificial Intelligence Platform OpenEvidence to Augment Clinical Decision-Making for Primary Care PhysiciansRyan T. Hurt; Christopher R. Stephenson; Elizabeth A. Gilman; et al. 2025. Journal of Primary Care & Community Healthjournal articleCited in: AI in Medical Education
- Effectiveness of generative artificial intelligence-based teaching versus traditional teaching methods in medical education: a meta-analysis of randomized controlled trialsJuan Li; Kaiyu Yin; Yida Wang; et al. 2025. BMC Medical Educationjournal articleCited in: AI in Medical Education
- Large Language Model–Based Virtual Patient Systems for History-Taking in Medical Education: Comprehensive Systematic ReviewDongliang Li; Syaheerah Lebai Lutfi. 2026. JMIR Medical Informaticsjournal articleCited in: AI in Medical Education
- NHS Topol ReviewCited in: AI in Medical Education
- Reconciling how clinical reasoning is learned in the age of artificial intelligenceAriel Yuhan Ong; Margaret Sui; Kyra L. Rosen; et al. 2026. npj Digital Medicinejournal articleCited in: AI in Medical Education
- Effectiveness of Informed AI Use on Clinical Competence of General Practitioners and Internists: Pre-Post Intervention StudyEyad A Qunaibi; Ayman M Al-Qaaneh; Baraa F Ismail; et al. 2026. JMIR Medical Educationjournal articleCited in: AI in Medical Education
- Regulation (EU) 2024/1689, Article 4Cited in: AI in Medical Education
- Artificial intelligence in undergraduate medical education: an updated scoping reviewJennifer Simoni; Judith Urtubia-Fernandez; Elisa Mengual; et al. 2025. BMC Medical Educationjournal articleCited in: AI in Medical Education (passage 1); AI in Medical Education (passage 2); AI in Medical Education (passage 3)Recommended in: AI in Medical Education (passage 4)
The Physician-AI Partnership: Future Perspectives
References
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- Fidelity of Medical Reasoning in Large Language ModelsSuhana Bedi; Yixing Jiang; Philip Chung; et al. 2025. JAMA Network Openjournal articleCited in: The Physician-AI Partnership: Future Perspectives
- FDA AI-Enabled Medical DevicesCited in: The Physician-AI Partnership: Future Perspectives (passage 1); The Physician-AI Partnership: Future Perspectives (passage 2)
- Viz.ai Implementation of Stroke Augmented Intelligence and Communications Platform to Improve Indicators and Outcomes for a Comprehensive Stroke Center and NetworkM.E. Figurelle; D.M. Meyer; E.S. Perrinez; et al. 2023. American Journal of Neuroradiologyjournal articleCited in: The Physician-AI Partnership: Future Perspectives
- Interval cancer, sensitivity, and specificity comparing AI-supported mammography screening with standard double reading without AI in the MASAI study: a randomised, controlled, non-inferiority, single-blinded, population-based, screening-accuracy trialJessie Gommers; Veronica Hernström; Viktoria Josefsson; et al. 2026. The Lancetjournal articleCited in: The Physician-AI Partnership: Future Perspectives
- IBM, 2022Cited in: The Physician-AI Partnership: Future Perspectives (passage 1); The Physician-AI Partnership: Future Perspectives (passage 2)
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- Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided DetectionConstance D. Lehman; Robert D. Wellman; Diana S. M. Buist; et al. 2015. JAMA Internal Medicinejournal articleCited in: The Physician-AI Partnership: Future Perspectives
- Liu et al., 2021Cited in: The Physician-AI Partnership: Future Perspectives
- Lång et al., 2025Cited in: The Physician-AI Partnership: Future Perspectives
- Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment TimesJuan Carlos Martinez-Gutierrez; Youngran Kim; Sergio Salazar-Marioni; et al. 2023. JAMA Neurologyjournal articleCited in: The Physician-AI Partnership: Future Perspectives
- scheduled to take effect October 1, 2026Cited in: The Physician-AI Partnership: Future Perspectives
- Somashekhar et al., 2018Cited in: The Physician-AI Partnership: Future Perspectives
- Somashekhar et al., 2018Cited in: The Physician-AI Partnership: Future Perspectives
- AI-based chest X-ray prioritization in the lung cancer diagnostic pathway: the LungIMPACT randomized controlled trialNick Woznitza; Lesley Smith; Janette Rawlinson; et al. 2026. Nature Medicinejournal articleCited in: The Physician-AI Partnership: Future Perspectives
- You et al., 2020Cited in: The Physician-AI Partnership: Future Perspectives
Appendices
References
Recommended reading
- physicianaihandbook.comRecommended in: References
Quick Reference: All Chapter Summaries (TL;DRs)
References
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: Quick Reference: All Chapter Summaries (TL;DRs)
- The Parable of Google Flu: Traps in Big Data AnalysisDavid Lazer; Ryan Kennedy; Gary King; et al. 2014. Sciencejournal articleCited in: Quick Reference: All Chapter Summaries (TL;DRs)
- Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy studyKristina Lång; Viktoria Josefsson; Anna-Maria Larsson; et al. 2023. The Lancet Oncologyjournal articleCited in: Quick Reference: All Chapter Summaries (TL;DRs)
- Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment TimesJuan Carlos Martinez-Gutierrez; Youngran Kim; Sergio Salazar-Marioni; et al. 2023. JAMA Neurologyjournal articleCited in: Quick Reference: All Chapter Summaries (TL;DRs)
- Privacy-Preserving Surgical Video Analysis with Swarm Learning — Results from a Multinational Appendectomy CohortOliver Lester Saldanha; Kevin Pfeiffer; Sebastian Bodenstedt; et al. 2026. NEJM AIjournal articleCited in: Quick Reference: All Chapter Summaries (TL;DRs)
- Wong et al., 2021Cited in: Quick Reference: All Chapter Summaries (TL;DRs)
- Antimicrobial Selection by a ComputerVictor L. Yu. 1979. JAMAjournal articleCited in: Quick Reference: All Chapter Summaries (TL;DRs)
Glossary of Medical AI Terms
References
- Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy: a multicentre, observational studyKrzysztof Budzyń; Marcin Romańczyk; Diana Kitala; et al. 2025. The Lancet Gastroenterology & Hepatologyjournal articleCited in: Glossary of Medical AI Terms
- Potentially harmful consequences of artificial intelligence (AI) chatbot use among patients with mental illness: Early data from a large psychiatric service systemSidse Godske Olsen; Christian Jon Reinecke-Tellefsen; Søren Dinesen Østergaard. 2025preprintCited in: Glossary of Medical AI Terms
- Pierre et al., 2025Cited in: Glossary of Medical AI Terms
- An algorithmic approach to reducing unexplained pain disparities in underserved populationsEmma Pierson; David M. Cutler; Jure Leskovec; et al. 2021. Nature Medicinejournal articleCited in: Glossary of Medical AI Terms
- Evaluation of Large Language Model Chatbot Responses to Psychotic PromptsElaine Shen; Fadi Hamati; Meghan Rose Donohue; et al. 2026. JAMA Psychiatryjournal articleCited in: Glossary of Medical AI Terms
Clinical Case Study Library
References
- Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care officesMichael D. Abràmoff; Philip T. Lavin; Michele Birch; et al. 2018. npj Digital Medicinejournal articleCited in: Clinical Case Study Library
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trialAmbrose Agweyu; Paul Mwaniki; Vaishnavi Menon; et al. 2026. Nature Medicinejournal articleCited in: Clinical Case Study Library
- Becker’s Hospital Review, January 2026Cited in: Clinical Case Study Library
- CMS, 2020Cited in: Clinical Case Study Library
- Economic impact of medication non-adherence by disease groups: a systematic reviewRachelle Louise Cutler; Fernando Fernandez-Llimos; Michael Frommer; et al. 2018. BMJ Openjournal articleCited in: Clinical Case Study Library
- Deseret News, January 2026Cited in: Clinical Case Study Library
- FDA DEN180001Cited in: Clinical Case Study Library
- FDA K252970Cited in: Clinical Case Study Library
- Google, 2008Cited in: Clinical Case Study Library
- January 2026 announcementCited in: Clinical Case Study Library
- A meta-analysis of Watson for Oncology in clinical applicationZhou Jie; Zeng Zhiying; Li Li. 2021. Scientific Reportsjournal articleCited in: Clinical Case Study Library
- The Parable of Google Flu: Traps in Big Data AnalysisDavid Lazer; Ryan Kennedy; Gary King; et al. 2014. Sciencejournal articleCited in: Clinical Case Study Library
- Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment TimesJuan Carlos Martinez-Gutierrez; Youngran Kim; Sergio Salazar-Marioni; et al. 2023. JAMA Neurologyjournal articleCited in: Clinical Case Study Library
- ChatGPT Health performance in a structured test of triage recommendationsAshwin Ramaswamy; Alvira Tyagi; Hannah Hugo; et al. 2026. Nature Medicinejournal articleCited in: Clinical Case Study Library
- Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scansMichael Roberts; Derek Driggs; Matthew Thorpe; et al. 2021. Nature Machine Intelligencejournal articleCited in: Clinical Case Study Library
- Sarhan et al., 2025Cited in: Clinical Case Study Library
- Utah Office of AI Policy, 2026Cited in: Clinical Case Study Library (passage 1); Clinical Case Study Library (passage 2)
- Wong et al., 2021Cited in: Clinical Case Study Library
- Concordance Study Between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in ChinaNa Zhou; Chuan-Tao Zhang; Hong-Ying Lv; et al. 2019. The Oncologistjournal articleCited in: Clinical Case Study Library
AI Tools by Medical Specialty
References
- De Novo DEN180001Cited in: AI Tools by Medical Specialty
- De Novo DEN180005Cited in: AI Tools by Medical Specialty (passage 1); AI Tools by Medical Specialty (passage 2)
- De Novo DEN250007Cited in: AI Tools by Medical Specialty
- FDA 510(k) overviewCited in: AI Tools by Medical Specialty
- FDA AI-Enabled Medical DevicesCited in: AI Tools by Medical Specialty (passage 1); AI Tools by Medical Specialty (passage 2); AI Tools by Medical Specialty (passage 3)
- FDA De Novo overviewCited in: AI Tools by Medical Specialty
- FDA decision letterCited in: AI Tools by Medical Specialty (passage 1); AI Tools by Medical Specialty (passage 2)
- FDA P250008Cited in: AI Tools by Medical Specialty
- FDA PMA overviewCited in: AI Tools by Medical Specialty
- FDA, DEN230008Cited in: AI Tools by Medical Specialty
- FDA, June 2023Cited in: AI Tools by Medical Specialty (passage 1); AI Tools by Medical Specialty (passage 2)
- FDA, P150046Cited in: AI Tools by Medical Specialty
- Derivation and independent validation of kidneyintelX .dkd: A prognostic test for the assessment of diabetic kidney disease progressionGirish N. Nadkarni; Sharon Stapleton; Dipti Takale; et al. 2023. Diabetes, Obesity and Metabolismjournal articleCited in: AI Tools by Medical Specialty
- Wong et al., 2021Cited in: AI Tools by Medical Specialty
AI Failures in Medicine: Lessons Learned
References
- Reliability of LLMs as medical assistants for the general public: a randomized preregistered studyAndrew M. Bean; Rebecca Elizabeth Payne; Guy Parsons; et al. 2026. Nature Medicinejournal articleCited in: AI Failures in Medicine: Lessons Learned
- Fidelity of Medical Reasoning in Large Language ModelsSuhana Bedi; Yixing Jiang; Philip Chung; et al. 2025. JAMA Network Openjournal articleCited in: AI Failures in Medicine: Lessons Learned
- A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic RetinopathyEmma Beede; Elizabeth Baylor; Fred Hersch; et al. 2020. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systemsconference paperCited in: AI Failures in Medicine: Lessons Learned
- Performance of a large language model on the reasoning tasks of a physicianPeter G. Brodeur; Thomas A. Buckley; Zahir Kanjee; et al. 2026. Sciencejournal articleCited in: AI Failures in Medicine: Lessons Learned
- Digital and online symptom checkers and health assessment/triage services for urgent health problems: systematic reviewDuncan Chambers; Anna J Cantrell; Maxine Johnson; et al. 2019. BMJ Openjournal articleCited in: AI Failures in Medicine: Lessons Learned
- Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissionsAlistair Connell; Hugh Montgomery; Peter Martin; et al. 2019. npj Digital Medicinejournal articleCited in: AI Failures in Medicine: Lessons Learned
- A meta-analysis of Watson for Oncology in clinical applicationZhou Jie; Zeng Zhiying; Li Li. 2021. Scientific Reportsjournal articleCited in: AI Failures in Medicine: Lessons Learned
- Kohn et al., 2000Cited in: AI Failures in Medicine: Lessons Learned
- The Parable of Google Flu: Traps in Big Data AnalysisDavid Lazer; Ryan Kennedy; Gary King; et al. 2014. Sciencejournal articleCited in: AI Failures in Medicine: Lessons Learned
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: AI Failures in Medicine: Lessons Learned
- Large Language Model Performance and Clinical Reasoning TasksArya S. Rao; Kaiz P. Esmail; Richard S. Lee; et al. 2026. JAMA Network Openjournal articleCited in: AI Failures in Medicine: Lessons Learned
- Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scansMichael Roberts; Derek Driggs; Matthew Thorpe; et al. 2021. Nature Machine Intelligencejournal articleCited in: AI Failures in Medicine: Lessons Learned
- M. D. Anderson Breaks With IBM Watson, Raising Questions About Artificial Intelligence in OncologyCharlie Schmidt. 2017. JNCI: Journal of the National Cancer Institutejournal articleCited in: AI Failures in Medicine: Lessons Learned
- Evaluation of symptom checkers for self diagnosis and triage: audit studyHannah L Semigran; Jeffrey A Linder; Courtney Gidengil; et al. 2015. BMJjournal articleCited in: AI Failures in Medicine: Lessons Learned
- Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction ModelAndrew Wong. 2026. JAMA Network Openjournal articleCited in: AI Failures in Medicine: Lessons Learned
- Wong et al., 2021Cited in: AI Failures in Medicine: Lessons Learned
- Wu et al., 2025, preprintCited in: AI Failures in Medicine: Lessons Learned
- Concordance Study Between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in ChinaNa Zhou; Chuan-Tao Zhang; Hong-Ying Lv; et al. 2019. The Oncologistjournal articleCited in: AI Failures in Medicine: Lessons Learned
Further Reading and Resources
References
- Artificial Intelligence in the Provision of Health Care: An American College of Physicians Policy Position PaperNadia Daneshvar; Deepti Pandita; Shari Erickson; et al. 2024. Annals of Internal Medicinejournal articleCited in: Further Reading and Resources
- To Do No Harm — and the Most Good — with AI in Health CareCarey Beth Goldberg; Laura Adams; David Blumenthal; et al. 2024. NEJM AIjournal articleCited in: Further Reading and Resources
- The Missing Dimension in Clinical AI: Making Hidden Values VisibleCarey Goldberg; Ran D. Balicer; Mamatha Bhat; et al. 2026. NEJM AIjournal articleCited in: Further Reading and Resources
- Noori et al., 2025, preprintCited in: Further Reading and Resources
- Large Language Models in Medicine: The Potentials and PitfallsJesutofunmi A. Omiye; Haiwen Gui; Shawheen J. Rezaei; et al. 2024. Annals of Internal Medicinejournal articleCited in: Further Reading and Resources
- Stanford Medicine, 2026Cited in: Further Reading and Resources
Recommended reading
- AI Resource HubRecommended in: Further Reading and Resources
- AMA Augmented Intelligence in MedicineRecommended in: Further Reading and Resources
- AMIA Annual SymposiumRecommended in: Further Reading and Resources
- CMS Innovation CenterRecommended in: Further Reading and Resources
- DynaMedex with Dyna AIRecommended in: Further Reading and Resources
- FDA AI-Enabled Medical DevicesRecommended in: Further Reading and Resources
- Harvard Bioethics - RAISE 2025Recommended in: Further Reading and Resources
- HIMSS Global Health ConferenceRecommended in: Further Reading and Resources
- Machine Learning for HealthRecommended in: Further Reading and Resources
- Nightingale Open ScienceRecommended in: Further Reading and Resources
- Regulation (EU) 2024/1689, EUR-LexRecommended in: Further Reading and Resources
- RSNA Annual MeetingRecommended in: Further Reading and Resources
- scheduled to take effect October 1, 2026Recommended in: Further Reading and Resources (passage 1); Further Reading and Resources (passage 2)
- State of Clinical AI ReportRecommended in: Further Reading and Resources
- State of Clinical AI Report 2026Recommended in: Further Reading and Resources
- State of Clinical AI Report 2026 (Google Slides)Recommended in: Further Reading and Resources
- WHO, 2021Recommended in: Further Reading and Resources (passage 1); Further Reading and Resources (passage 2)
AI Vendor Evaluation Framework
References
- A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic RetinopathyEmma Beede; Elizabeth Baylor; Fred Hersch; et al. 2020. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systemsconference paperCited in: AI Vendor Evaluation Framework
- Evaluation of a digitally-enabled care pathway for acute kidney injury management in hospital emergency admissionsAlistair Connell; Hugh Montgomery; Peter Martin; et al. 2019. npj Digital Medicinejournal articleCited in: AI Vendor Evaluation Framework
- A meta-analysis of Watson for Oncology in clinical applicationZhou Jie; Zeng Zhiying; Li Li. 2021. Scientific Reportsjournal articleCited in: AI Vendor Evaluation Framework
- Early Recalls and Clinical Validation Gaps in Artificial Intelligence–Enabled Medical DevicesBranden Lee; Patrick Kramer; Sara Sandri; et al. 2025. JAMA Health Forumjournal articleCited in: AI Vendor Evaluation Framework
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleCited in: AI Vendor Evaluation Framework
- ONC, HTI-1 Final RuleCited in: AI Vendor Evaluation Framework
- M. D. Anderson Breaks With IBM Watson, Raising Questions About Artificial Intelligence in OncologyCharlie Schmidt. 2017. JNCI: Journal of the National Cancer Institutejournal articleCited in: AI Vendor Evaluation Framework
- Multicenter Prospective Validation of an Updated Proprietary Sepsis Prediction ModelAndrew Wong. 2026. JAMA Network Openjournal articleCited in: AI Vendor Evaluation Framework
- Wong et al., 2021Cited in: AI Vendor Evaluation Framework
- Concordance Study Between IBM Watson for Oncology and Clinical Practice for Patients with Cancer in ChinaNa Zhou; Chuan-Tao Zhang; Hong-Ying Lv; et al. 2019. The Oncologistjournal articleCited in: AI Vendor Evaluation Framework
Recommended reading
- AMA eight-step health-system AI governance toolkitRecommended in: AI Vendor Evaluation Framework
- APA Practice OrganizationRecommended in: AI Vendor Evaluation Framework
- Brankovic et al., NPJ Digital Medicine, 2025Recommended in: AI Vendor Evaluation Framework
- chai.orgRecommended in: AI Vendor Evaluation Framework
- European Heart Journal Digital Health, 2022Recommended in: AI Vendor Evaluation Framework
- FDA AI-Enabled Medical DevicesRecommended in: AI Vendor Evaluation Framework
- https://amia.org/education-events/clinical-informatics-board-review-courseRecommended in: AI Vendor Evaluation Framework
- JMIRx Med, 2025Recommended in: AI Vendor Evaluation Framework
- Kwong et al., JAMA Network Open 2023Recommended in: AI Vendor Evaluation Framework
Clinical AI Policy Templates
Recommended reading
- AMA AI Principles, 2018Recommended in: Clinical AI Policy Templates
- American Medical Informatics Association GuidelinesRecommended in: Clinical AI Policy Templates
- chai.orgRecommended in: Clinical AI Policy Templates
- FDA AI-Enabled Medical DevicesRecommended in: Clinical AI Policy Templates
- HHS Business Associates GuidanceRecommended in: Clinical AI Policy Templates
- ONC, HTI-1 Final RuleRecommended in: Clinical AI Policy Templates
Medical AI Career Guide
References
Course Syllabus Template
References
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methodsGary S Collins; Karel G M Moons; Paula Dhiman; et al. 2024. BMJjournal articleCited in: Course Syllabus Template
- DOI: 10.5281/zenodo.18251405Cited in: Course Syllabus Template
- PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methodsKarel G M Moons; Johanna A A Damen; Tabea Kaul; et al. 2025. BMJjournal articleCited in: Course Syllabus Template
Recommended reading
- AMA Physician Innovation NetworkRecommended in: Course Syllabus Template
- AMA Policy on Augmented Intelligence in Health CareRecommended in: Course Syllabus Template
- Artificial Intelligence/Machine Learning-Based Software as a Medical Device Action PlanRecommended in: Course Syllabus Template
- Deep Medicine: How Artificial Intelligence Can Make Healthcare Human AgainRecommended in: Course Syllabus Template
- FDA AI-Enabled Medical DevicesRecommended in: Course Syllabus Template
- FDA, 2025Recommended in: Course Syllabus Template
- https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781307Recommended in: Course Syllabus Template
- Dissecting racial bias in an algorithm used to manage the health of populationsZiad Obermeyer; Brian Powers; Christine Vogeli; et al. 2019. Sciencejournal articleRecommended in: Course Syllabus Template
- TRIPOD statement websiteRecommended in: Course Syllabus Template
How to Cite This Handbook
Recommended reading
- physicianaihandbook.comRecommended in: How to Cite This Handbook