Peer-Reviewed Evidence for Every Specialty
February 2026
Clinical AI performance in real-world settings often falls short of published validation studies. This handbook helps physicians evaluate AI tools based on peer-reviewed evidence, written from a clinician’s perspective for clinicians, health system leaders, and anyone building or deploying clinical AI.
Three questions drive every chapter: What does the peer-reviewed evidence actually show? How do I evaluate claims against that evidence? What are the medico-legal implications when AI is wrong?
The handbook covers five areas: foundations of clinical AI, specialty-specific applications across all ACGME disciplines, implementation and evaluation frameworks, practical tools for daily practice, and future directions. You can read sequentially or jump to the section most relevant to your work.
Continuously updated as new research emerges.
Important Disclaimers
This handbook is for educational purposes only and does not constitute medical advice, diagnosis, or treatment. AI systems discussed herein are not substitutes for professional medical judgment.
Physicians remain solely responsible for clinical decisions, validating AI outputs before clinical use, ensuring regulatory compliance (FDA, HIPAA), and meeting the standard of care in their jurisdiction.
Information may become outdated given the rapidly evolving nature of AI technology. Verify recommendations with current clinical guidelines before application.
This handbook does not provide legal advice. Consult qualified legal counsel for malpractice and liability questions.
Select the pathway that matches your specialty and immediate needs:
“I need practical AI tools for my daily practice”
Start here:
“Radiology, Pathology, Dermatology, Ophthalmology”
Start here:
“General Surgery, Orthopedics, Neurosurgery, OBGYN”
Start here:
“Internal Medicine, Cardiology, Oncology, Neurology”
Start here:
“I work in fast-paced, high-stakes clinical environments”
Start here:
“I care for pediatric and newborn patients”
Start here:
The Physician AI Handbook is a clinical reference built on peer-reviewed evidence, covering AI applications across all ACGME-recognized medical specialties. Open access (CC BY 4.0).
AI history in medicine, fundamentals, clinical data challenges
Chapters 1-3 | Start here if new to AI
Key topics: Medical AI history (MYCIN to modern deep learning), AI fundamentals for clinicians, EHR data quality, clinical datasets
AI applications across all ACGME-recognized medical specialties
Chapters 4-22 | Jump to your specialty
Key topics: Radiology, Internal Medicine, Surgery, Anesthesiology, Pediatrics, OBGYN, Emergency Medicine, Critical Care, Oncology, Cardiology, Neurology, Psychiatry, Primary Care, Pathology, Dermatology, Ophthalmology, Orthopedics, Infectious Diseases, and more
Clinical deployment, ethics, privacy, safety, liability
6 chapters | Critical for implementation
Key topics: Evaluating AI tools, medical ethics & equity, HIPAA compliance, clinical AI safety, workflow integration, medical liability & malpractice
Hands-on guidance for AI in daily practice
4 chapters | Immediately applicable
Includes: AI toolkit for physicians, LLMs in clinical practice (ChatGPT, Claude, Copilot), AI-assisted documentation (ambient scribes), clinical research with AI
Emerging technologies, policy, global health, future perspectives
5 chapters | Forward-looking
Topics: Emerging AI technologies, global health equity, healthcare policy & governance, medical misinformation, the physician-AI partnership
This handbook is part of a series on AI in health:
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
You are free to: Share, adapt, and use this material for any purpose, including commercial use, with attribution.
Full license details | CC BY 4.0 Legal Code
Tegomoh, B. (2025). The Physician AI Handbook: Peer-Reviewed Evidence for Every Specialty. DOI: 10.5281/zenodo.18251405. URL: physicianaihandbook.com