flowchart LR
A[Part I:<br/>Foundations] --> B[Part II:<br/>Clinical<br/>Specialties]
B --> C[Part III:<br/>Implementation]
C --> D[Part IV:<br/>Practical Tools]
D --> E[Part V:<br/>Future]
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click A "/foundations/history.html"
click B "/specialties/radiology.html"
click C "/implementation/evaluation.html"
click D "/practical/toolkit.html"
click E "/future/emerging.html"
The Physician AI Handbook
Peer-Reviewed Evidence for Every Specialty
What works. What doesn’t. What happens when AI is wrong.
Welcome to The Physician AI Handbook
Clinical AI performance in real-world settings often falls short of published validation studies. Peer-reviewed evidence, not vendor marketing, should drive clinical adoption. 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 you evaluate claims against that evidence? What are the medico-legal implications when AI is wrong?
This resource is continuously updated as new research emerges.
Important Disclaimers
This handbook is for educational and informational purposes only. It does not provide medical advice, diagnosis, or treatment. AI systems discussed here are decision-support tools, not substitutes for clinical judgment.
Clinical use remains context-specific. Physicians should validate AI outputs before clinical use, follow applicable FDA and HIPAA requirements, and meet the standard of care in their jurisdiction.
Information may become outdated as AI tools, evidence, and clinical guidelines change. Verify recommendations with current clinical guidance before application.
This handbook does not provide legal advice. Consult qualified legal counsel for questions about malpractice, liability, and regulatory compliance.
Start Here
Start with the evidence framework, then move to the clinical tool layer. This path shows how the handbook evaluates clinical AI claims before adoption.
- Executive Summary: Key findings across all specialties
- AI Fundamentals for Clinicians: What AI actually is and how it works
- AI Physician Toolkit: Practical tools for daily practice
Then continue to Evaluating AI Systems before adopting any tool.
For specialty-specific reading paths, see the Preface.
Book Structure
- Part I: Foundations (Chapters 1–3) – AI history in medicine, fundamentals, clinical data challenges
- Part II: Clinical Specialties (Chapters 4–22) – AI across all ACGME-recognized specialties
- Part III: Implementation (6 chapters) – Evaluation, ethics, privacy, safety, workflow, liability
- Part IV: Practical Tools (4 chapters) – Toolkit, LLMs in practice, documentation, clinical research
- Part V: Future (6 chapters) – Emerging tech, global health, policy, misinformation, medical education, physician-AI partnership
Companion Handbooks
The Public Health AI Handbook
AI applications across population health: disease surveillance, epidemic forecasting, genomic pathogen analysis, outbreak detection, health department implementation, deployment failures, AI-assisted coding for epidemiological analysis, behavioral interventions, and health misinformation. For epidemiologists, public health practitioners, and health department leaders.
The Biosecurity Handbook
Where AI capability meets biological risk: laboratory biosafety, the Biological Weapons Convention, dual-use research oversight, DNA synthesis screening, AI-enabled pathogen design risks, LLM information hazards, red-teaming, autonomous lab agents, and governance frameworks for AI-bio convergence. For biosecurity professionals, AI safety researchers, policymakers, and laboratory personnel.
The Life Sciences AI Handbook
AI for biomedical discovery, molecular design, cellular systems, laboratory automation, and translational research. For researchers, biotechnology teams, computational biologists, physician-scientists, and students evaluating AI systems across molecules, cells, experiments, and therapeutic development.
License & Citation
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
You are free to: Share, copy, redistribute, adapt, remix, and build upon this material for any purpose, including commercially, with attribution.
Full license details | CC BY 4.0 Legal Code
How to Cite
Tegomoh, B. (2025). The Physician AI Handbook: Peer-Reviewed Evidence for Every Specialty. DOI: 10.5281/zenodo.18251405. URL: physicianaihandbook.com
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Using this handbook in research or teaching? See the Citation Guide for AMA, APA, Vancouver, and BibTeX formats, and the Clinical Case Study Library for real-world AI deployment examples.