Peer-Reviewed Evidence for Every Specialty
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?
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.
This resource is 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Information may become outdated given the rapidly evolving nature of AI technology.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:
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]
style A fill:#ffffff,stroke:#0FB5BA,stroke-width:2px,color:#334155
style B fill:#ffffff,stroke:#0FB5BA,stroke-width:2px,color:#334155
style C fill:#ffffff,stroke:#0FB5BA,stroke-width:2px,color:#334155
style D fill:#ffffff,stroke:#0FB5BA,stroke-width:2px,color:#334155
style E fill:#ffffff,stroke:#0FB5BA,stroke-width:2px,color:#334155
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"
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
Tegomoh, B. (2025). The Physician AI Handbook: Peer-Reviewed Evidence for Every Specialty. DOI: 10.5281/zenodo.18251405. URL: physicianaihandbook.com
