Appendix F — Appendix F: Resources for Further Learning
Introduction
This appendix provides curated resources for physicians seeking to deepen their understanding of medical AI. Resources are organized by category: journals, websites, courses, conferences, books, and professional organizations.
Academic Journals
AI-Focused Medical Journals
JAMA Health Forum: Digital Health & AI - Publisher: American Medical Association - Focus: Clinical AI applications, digital health, health informatics - Website: jamanetwork.com - Open Access: Selective
The Lancet Digital Health - Publisher: The Lancet - Focus: Digital health technologies, AI, telemedicine - Website: thelancet.com/journals/landig - Open Access: Yes
npj Digital Medicine (Nature Portfolio) - Publisher: Nature - Focus: Digital medicine, AI, wearables, precision health - Website: nature.com/npjdigitalmed - Open Access: Yes
Journal of Medical Internet Research (JMIR) - Focus: eHealth, digital health, AI applications - Website: jmir.org - Open Access: Yes
Traditional Medical Journals with AI Content
Nature Medicine - Regular AI/ML research publications - Website: nature.com/nm
NEJM AI (New England Journal of Medicine) - Launched 2024, dedicated AI supplement - Website: nejm.org
Radiology: Artificial Intelligence - Publisher: RSNA (Radiological Society of North America) - Focus: Imaging AI - Website: pubs.rsna.org/journal/ai
JACC: Cardiovascular Imaging - Frequent cardiac AI publications - Website: imaging.onlinejacc.org
Websites and Online Resources
Regulatory and Government
FDA: AI/ML in Medical Devices - Comprehensive resource on regulatory framework - Lists all FDA-cleared/approved AI medical devices - Website: fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
WHO: Ethics and Governance of AI for Health - Global guidance on AI ethics, regulation - Website: who.int (search “AI ethics health”)
CMS Innovation Center - Medicare/Medicaid AI reimbursement, value-based care models - Website: innovation.cms.gov
Professional Societies
ACR Data Science Institute (American College of Radiology) - AI use cases, validation standards, AI-LAB accreditation - Website: acrdsi.org
AMA: Augmented Intelligence in Medicine - Policy statements, resources for physicians - Website: ama-assn.org (search “augmented intelligence”)
AMIA (American Medical Informatics Association) - Healthcare informatics, AI, data science - Website: amia.org
Society for Imaging Informatics in Medicine (SIIM) - Imaging AI, PACS integration - Website: siim.org
Educational Platforms
Stanford AI in Healthcare Specialization (Coursera) - Online courses from Stanford faculty - Topics: AI foundations, imaging, NLP, deployment - Website: coursera.org/specializations/ai-healthcare
MIT OpenCourseWare: Machine Learning in Healthcare - Free lectures, assignments from MIT course - Website: ocw.mit.edu
fast.ai: Practical Deep Learning for Coders - Practical, hands-on deep learning course - Medical imaging examples included - Website: fast.ai
Google AI Healthcare - Research papers, blog posts, tools - Website: health.google/health-research
News and Commentary
The Health Care Blog - Industry commentary, AI analysis - Website: thehealthcareblog.com
STAT News: Artificial Intelligence Section - Healthcare journalism, AI coverage - Website: statnews.com/tag/artificial-intelligence
AI in Medicine Newsletter (Multiple publishers) - Weekly/monthly roundups of AI news, papers - Various free newsletters available
Online Courses and Training
Beginner-Friendly
AI for Everyone (Andrew Ng, Coursera) - Non-technical introduction to AI - No coding required - Duration: ~6 hours
AI in Healthcare (Stanford Online) - Overview of healthcare AI applications - Case studies, ethics discussions - Duration: Self-paced
Intermediate
Machine Learning (Andrew Ng, Coursera) - Classic ML course, technical but accessible - Python coding required - Duration: ~60 hours
Deep Learning Specialization (deeplearning.ai) - Neural networks, CNNs, sequence models - Healthcare examples included - Duration: ~3 months (10 hours/week)
Advanced
CS231n: Convolutional Neural Networks for Visual Recognition (Stanford) - Deep dive into image recognition, medical imaging AI - Lectures available free on YouTube - Website: cs231n.stanford.edu
CS224n: Natural Language Processing (Stanford) - NLP fundamentals, clinical text applications - Website: web.stanford.edu/class/cs224n
Conferences and Events
Major Medical AI Conferences
RSNA (Radiological Society of North America) Annual Meeting - World’s largest radiology conference, extensive AI content - Location: Chicago, IL (annual, November) - Website: rsna.org
HIMSS (Healthcare Information and Management Systems Society) - Health IT, AI, digital health - Location: Varies (annual, spring) - Website: himss.org
AMIA Annual Symposium - Medical informatics, AI research - Location: Varies (annual, November) - Website: amia.org/amia2024
ML4H (Machine Learning for Health) - Academic conference, cutting-edge AI research - Often co-located with NeurIPS - Website: ml4health.org
Specialty-Specific
AI in Radiology (multiple conferences) - SIIM Conference on Machine Intelligence in Medical Imaging - European Congress of Radiology (ECR) AI sessions
AI in Pathology - Digital Pathology & AI Congress - Pathology Informatics Summit
AI in Cardiology - ACC (American College of Cardiology) AI sessions - AHA (American Heart Association) scientific sessions
Books
For Physicians (Non-Technical)
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again - Author: Eric Topol - Focus: AI’s potential to improve care, physician-patient relationships - Level: Accessible, visionary
The AI Revolution in Medicine: GPT-4 and Beyond - Authors: Peter Lee, Carey Goldberg, Isaac Kohane - Focus: Large language models in healthcare, GPT-4’s capabilities - Level: Accessible
Artificial Intelligence in Healthcare - Author: Adam Bohr, Kaveh Memarzadeh - Focus: Overview of AI applications across specialties - Level: Accessible overview
For Technical Readers
Deep Learning for Medical Image Analysis - Editors: S. Kevin Zhou, Hayit Greenspan, Dinggang Shen - Focus: Imaging AI technical foundations - Level: Advanced (requires ML background)
Machine Learning for Healthcare - Editors: David Sontag, Finale Doshi-Velez, Marzyeh Ghassemi - Focus: Technical ML methods for clinical data - Level: Advanced
Evaluating Machine Learning Models: A Beginner’s Guide - Author: Alice Zheng - Focus: Model evaluation, validation techniques - Level: Intermediate technical
Ethics and Policy
Weapons of Math Destruction - Author: Cathy O’Neil - Focus: Algorithmic bias, societal impact (not healthcare-specific but relevant) - Level: Accessible
The Ethical Algorithm - Authors: Michael Kearns, Aaron Roth - Focus: Fairness, privacy in algorithms - Level: Accessible with some technical content
Professional Organizations and Societies
American Medical Informatics Association (AMIA) - Membership for clinicians, researchers in health IT/AI - Benefits: Conferences, journals, networking - Website: amia.org
ACR Data Science Institute - Radiology AI focus, but resources applicable broadly - AI-LAB accreditation program for vendor transparency - Website: acrdsi.org
The Society for Imaging Informatics in Medicine (SIIM) - Imaging informatics, AI, PACS - Website: siim.org
Healthcare Information and Management Systems Society (HIMSS) - Broad health IT, includes AI working groups - Website: himss.org
American College of Physicians (ACP): Digital Health - Internal medicine perspective on AI - Website: acponline.org
Podcasts
The AI in Medicine Podcast - Host: Pranav Rajpurkar (Harvard Medical School) - Focus: Interviews with AI researchers, clinicians
Health Tech Nerds - Hosts: Gabe Tweeten, Jared Johnson - Focus: Health IT, AI, digital health industry
This Week in Health IT - Host: Bill Russell - Focus: Health IT news, AI developments
The Digital Health Podcast - Various hosts - Focus: Digital health innovations, AI applications
GitHub Repositories and Code Resources
Medical Image Analysis Resources - Various open-source medical imaging AI projects - Search GitHub for “medical imaging deep learning”
CheXNet (Stanford) - Open-source chest X-ray classification model - Website: stanfordmlgroup.github.io
TensorFlow Medical Imaging - Google’s TensorFlow medical imaging tutorials - Website: tensorflow.org
PyTorch Medical Imaging - Medical imaging examples using PyTorch - Website: pytorch.org
Note: Code repositories for educational purposes—not for clinical deployment without validation.
Regulatory Guidance Documents
FDA Software as a Medical Device (SaMD) Framework - Comprehensive regulatory guidance - Website: fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd
EU AI Act - European Union AI regulation text - Website: ec.europa.eu (search “AI Act”)
WHO AI Ethics Guidelines - Global ethical framework for health AI - Website: who.int (search “AI ethics”)
Data Science and ML Fundamentals
For Self-Study
Khan Academy: Statistics and Probability - Free, foundational statistics - Website: khanacademy.org
StatQuest (YouTube) - Excellent intuitive explanations of ML concepts - Host: Josh Starmer - Website: youtube.com/statquest
3Blue1Brown: Neural Networks - Beautiful visual explanations of deep learning - Website: youtube.com/3blue1brown
Staying Current
How to stay informed in rapidly evolving field:
- Follow key researchers on Twitter/X: Eric Topol, Andrew Ng, Fei-Fei Li, Ziad Obermeyer, others
- Subscribe to arXiv alerts: Daily ML/AI papers (arxiv.org, search “cs.LG” or “cs.CV”)
- Join LinkedIn groups: Healthcare AI, Medical Informatics groups
- Attend local meetups: Many cities have healthcare AI meetups
- Participate in online forums: r/MachineLearning, r/HealthIT on Reddit
- Set Google Scholar alerts: For topics of interest (e.g., “radiology AI,” “clinical NLP”)
Conclusion
This resource list is starting point, not exhaustive catalog. The field evolves rapidly—new journals launch, courses update, conferences emerge. Physicians committed to AI literacy should:
- Diversify sources: Read both technical and clinical perspectives
- Maintain skepticism: Not all resources equal quality; evaluate critically
- Engage actively: Attend conferences, ask questions, network with experts
- Contribute: Share your clinical insights with AI developers, researchers
- Stay curious: Medical AI is journey, not destination—continuous learning essential
Final recommendation: Don’t try to learn everything. Focus on areas relevant to your specialty and practice. Understanding AI principles, evaluating evidence, and recognizing limitations is more important than mastering technical details.
The future of medicine requires physicians who understand both healing and technology. These resources support your journey toward that synthesis.