Appendix J: Resources for Further Learning

Medical AI education is distributed across peer-reviewed journals, regulators, professional societies, courses, conferences, datasets, and technical communities. The strongest learning plan combines primary evidence with specialty guidance and treats prices, schedules, product names, and commercial availability as time-sensitive details that must be checked at the source.

Learning Objectives

After reviewing this resource directory, readers should be able to:

  1. Select primary journals, regulatory sources, professional societies, and technical resources for a clinical AI question.
  2. Distinguish peer-reviewed evidence from reports, commentary, courses, vendor material, and preprints.
  3. Verify current course, conference, product, and membership details before making a time or financial commitment.
  4. Build a specialty-specific learning plan without treating any directory as exhaustive or permanently current.
TL;DR

Top journals: Lancet Digital Health, npj Digital Medicine, JMIR, JAMIA

Course routes: Stanford health-AI offerings, MIT OpenCourseWare, AMIA 10x10, and technical foundations from fast.ai or university courses. Titles, schedules, prerequisites, and prices should be verified on official pages.

Key conferences: AMIA Annual Symposium, ML4H (Machine Learning for Health), RSNA AI

Professional organizations: AMIA, HIMSS, ACR Data Science Institute

Staying current: Journal alerts, PubMed and Google Scholar alerts, regulator updates, professional-society guidance, and carefully selected newsletters

Introduction

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: Springer 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)

  • NEJM AI, launched in 2024, is a standalone peer-reviewed journal dedicated to AI in medicine.
  • Website: ai.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

  • Primary U.S. route for public AI-enabled medical-device records
  • FDA states that the list is periodically updated and not comprehensive; each entry links to a submission record
  • Website: FDA AI-Enabled Medical Devices

WHO: Ethics and Governance of AI for Health

CMS Innovation Center

  • Medicare and Medicaid payment and service-delivery model resources; it is not a dedicated AI reimbursement database
  • Website: CMS Innovation Center

Professional Societies

ACR Data Science Institute (American College of Radiology)

AMA: Augmented Intelligence in Medicine

AMIA (American Medical Informatics Association)

  • Healthcare informatics, AI, data science
  • Membership for clinicians and researchers in health IT and AI; benefits include conferences, journals, and networking
  • 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

Research Data Platforms

Nightingale Open Science

  • Nonprofit platform providing de-identified medical imaging data linked to patient outcomes
  • Its datasets are structured around unsolved clinical problems rather than around replicating expert labels.
  • Co-founded by Ziad Obermeyer (UC Berkeley) and Sendhil Mullainathan (University of Chicago)
  • Funded by Schmidt Futures
  • Website: Nightingale Open Science (formerly ngsci.org)

PhysioNet

  • Open-access archive of physiological signals and clinical data
  • Includes MIMIC-III/IV critical care databases
  • Website: physionet.org

Online Courses and Training

Beginner-Friendly

AI for Everyone (Andrew Ng, Coursera)

  • Non-technical introduction to AI
  • No coding required
  • Pace and availability: Verify on the current official course page

AI in Healthcare (Stanford Online)

  • Overview of healthcare AI applications
  • Case studies, ethics discussions
  • Format, title, and schedule: Verify on the current Stanford or Coursera page

Intermediate

Machine Learning (Andrew Ng, Coursera)

  • Technical machine-learning route whose title and platform have changed over time
  • Python coding required
  • Pace, prerequisites, and current syllabus: Verify on the official course page

Deep Learning Specialization (deeplearning.ai)

  • Neural networks, CNNs, sequence models
  • Healthcare examples included
  • Pace, modules, and platform terms: Verify on the official course page

Advanced

CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)

  • Image recognition foundations that apply to medical imaging AI
  • Lectures available free on YouTube
  • Website: cs231n.stanford.edu

CS224n: Natural Language Processing with Deep Learning (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

  • Large radiology meeting with substantial imaging-AI content
  • Historical cadence: Chicago, often late in the year; verify the current meeting dates and venue
  • Website: RSNA Annual Meeting

HIMSS (Healthcare Information and Management Systems Society)

AMIA Annual Symposium

  • Medical informatics, AI research
  • Location and dates vary; verify the current event page
  • Website: AMIA Annual Symposium

ML4H (Machine Learning for Health)

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

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

  • Editors: 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)

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

AMIA, the ACR Data Science Institute, and the Society for Imaging Informatics in Medicine (SIIM) are listed under Professional Societies above.

Healthcare Information and Management Systems Society (HIMSS)

  • Broad health IT, includes AI working groups
  • Website: himss.org

American College of Physicians (ACP): AI Resource Hub

  • AI Resource Hub: Curated AI resources for internists
  • Generative AI for Internal Medicine Physicians course (self-paced primer)
  • AI-powered patient simulation tools for practice
  • DynaMedex with Dyna AI: Clinical decision support with AI integration (free for ACP members)
  • ACP Policy Position Paper on AI: Official recommendations (Annals of Internal Medicine, 2024)
  • LLMs in Medicine Review: Comprehensive narrative review of potentials and pitfalls (Annals, 2024)

Podcasts

Podcast names, hosts, feeds, and publication status change frequently. The entries below are discovery leads; verify the current feed and editorial provenance before relying on an episode.

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

A Question of Science: How is AI transforming medicine? (Francis Crick Institute / BBC Studios, 2026)

  • Host: Brian Cox. Panel: Mihaela van der Schaar, Indra Joshi, Patrick Vallance, Samra Turajlic
  • Focus: audience questions on diagnosis, ambient scribing, bias, pandemics, drug discovery, and data protection, recorded live at the Crick on 1 July 2026
  • Video: youtube.com/watch?v=pVDCjMS5vtQ

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

These code repositories serve educational purposes and are not suitable for clinical deployment without validation.

Annual Reports and Syntheses

Annual synthesis reports provide evidence-driven overviews of clinical AI developments. They help physicians distinguish real-world progress from technological hype.

State of Clinical AI Report (ARISE Network) {#sec-state-of-clinical-ai}

  • Producer: Stanford-Harvard ARISE (AI Research and Science Evaluation) Network
  • Published: January 2026 (inaugural annual report)
  • Focus: Synthesis of significant developments, evidence, and emerging challenges in clinical AI
  • Authors: Peter Brodeur MD, Ethan Goh MD, Adam Rodman MD, Jonathan H. Chen MD PhD
  • Report slides: State of Clinical AI Report 2026 (Google Slides)
  • Website: arise-ai.org/report
  • Peer-reviewed version: a 2026 narrative review in BMJ Digital Health & AI restates the report across six domains from 82 studies, 42 of them nominated by the author team or by outside clinicians and researchers rather than found by database search, and its authors place equity, regulation, and implementation science outside its scope (Worth et al., 2026)
  • Stanford coverage: Clinical AI Has Boomed

Key themes summarized in the 2026 report and its Stanford Medicine coverage:

  • A review of more than 500 studies found that nearly half evaluated models using medical exam-style questions and 5% used real patient data; the figures are attributed to the report’s summary of the underlying review, not to all medical-AI publications (Stanford Medicine, 2026)
  • The report emphasizes assisted clinical workflows, while also documenting that incorrect AI advice can worsen decisions when users over-rely on it.
  • Strong performance on controlled benchmarks may not transfer to uncertain, incomplete, multi-step clinical work
  • Patient-facing AI requires outcome measurement, not just engagement metrics

RAISE Consortium (Responsible AI for Social and Ethical Healthcare) {#sec-raise-consortium}

  • Producer: Harvard Medical School, MaineHealth, Roux Institute at Northeastern
  • Symposia: 2023 (Cape Neddick, Maine), 2025 (Portland, Maine)
  • Focus: Ethical frameworks for clinical AI, value alignment, policy development
  • Key outputs:
  • MedLog proposal: Protocol for event-level logging of clinical AI (Noori et al., 2025, preprint)
  • Website: Harvard Bioethics - RAISE 2025

Key themes from RAISE 2025:

  • AI systems embed hidden value frameworks (fee-for-service vs. cost-containment, autonomy vs. paternalism)
  • Values embedded in a model may be difficult for physicians and patients to identify without explicit disclosure
  • A single organization may not contain all of the technical, clinical, and ethical expertise needed for useful value disclosure
  • The symposium recommended parallel tracks: public debate on AI values and pilot projects in leading health systems

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

WHO guidance on the ethics and governance of AI for health is listed under Regulatory and Government above.

Data Science and ML Fundamentals

For Self-Study

Khan Academy: Statistics and Probability

  • Free, foundational statistics
  • Website: khanacademy.org

StatQuest (YouTube)

  • Intuitive explanations of ML concepts
  • Host: Josh Starmer
  • Website: youtube.com/statquest

3Blue1Brown: Neural Networks

  • Visual explanations of deep learning
  • Website: youtube.com/3blue1brown

Staying Current

How to stay informed in a rapidly evolving field:

  1. Follow primary-source researchers and institutions: Use current professional profiles and verify claims against the underlying publication or official record.
  2. Subscribe to arXiv alerts: Daily ML/AI papers (arxiv.org, search “cs.LG” or “cs.CV”)
  3. Use professional networks selectively: Society communities and well-moderated professional groups can surface leads, but social posts are not evidence.
  4. Attend local meetups: Many cities have healthcare AI meetups
  5. Participate in online forums: r/MachineLearning, r/HealthIT on Reddit
  6. Set Google Scholar alerts: For topics of interest (e.g., “radiology AI,” “clinical NLP”)

Conclusion

No resource list is exhaustive. 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: Resource type determines what a source can support; a course, report, press article, preprint, regulatory record, and peer-reviewed trial are not interchangeable.
  • Engage actively: Attend conferences, ask questions, network with experts
  • Contribute: Share your clinical insights with AI developers, researchers
  • Stay curious: Medical AI changes quickly, and continuous learning is essential

Final recommendation: A focused learning plan should begin with the reader’s specialty and decision responsibilities. Understanding AI principles, evaluating evidence, and recognizing limitations are more important than collecting product names or mastering every technical detail.