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Information may become outdated given the rapidly evolving nature of AI technology.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.
Quick Start: Choose Your Path
Select the pathway that matches your specialty and immediate needs:
Claims require peer-reviewed citations; vendor marketing labeled as such
Limitations and failures documented alongside successes
No vendor funding or commercial relationships
Book Structure
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"
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