How are you using this handbook? Anonymous is fine. Tell me how
Artificial intelligence is entering clinical medicine as a new category of tool, and it deserves the scrutiny physicians apply to any clinical intervention: what was tested, in whom, against what comparator, and with what outcome.
Algorithms interpret chest radiographs. Deep learning models analyze pathology slides. Natural language processing extracts information from electronic health records. But published performance does not by itself establish clinical utility in a local workflow. Promotional claims can exceed the independent evidence for the exact product, version, population, and intended use. Even authorized or well-validated systems require careful implementation, explicit monitoring, and honest acknowledgment of limitations.
This handbook addresses the challenge physicians face: how to critically evaluate these tools, integrate them responsibly into clinical workflow, understand their limitations, and decide which applications genuinely improve patient care.
This is a field guide for evidence-based AI evaluation, not a computer science textbook. It explains as much of the technology as a physician needs to judge a claim about it.
This handbook prioritizes peer-reviewed evidence from major medical journals, exact FDA authorization records, and real-world implementations over press releases and vendor whitepapers. Where evidence clearly supports specific tools, I name them. Where products have documented limitations or failures, I describe the evidence and its boundaries. Physicians deserve honest assessments, not diplomatic neutrality between well-supported and unsupported claims.
Citations are checked against Crossref, PubMed, publisher records, or official organizational sources where available, and FDA status against FDA’s own records. Performance figures that come only from a vendor are identified as vendor-reported. When a claim proves outdated, overstated, or unsupported, it is corrected in place or bounded to what the evidence shows.
You do not need to read sequentially. Jump directly to your specialty chapter. Use the search function. Read the TL;DR summaries for quick orientation. Read the full chapters when evaluating tools for your practice.
Three approaches:
Every chapter begins with an expandable Chapter Summary (TL;DR) containing clinical context, key evidence, what works, what does not, and the clinical bottom line. Use the summary for orientation, then read the underlying evidence before making an implementation decision.
Stay skeptical. Match the strength of the claim to the study design, and do not treat retrospective validation as proof of clinical benefit. Professional duties remain, while legal responsibility for AI-assisted care depends on the jurisdiction, intended use, institutional controls, and facts of the case. See Physician AI Liability and Regulatory Compliance.
Select the pathway that matches your specialty and immediate needs:
Primary Care, Family Medicine, Outpatient Practice
Start here:
Radiology, Pathology, Dermatology, Ophthalmology
Start here:
General Surgery, Orthopedics, Neurosurgery, OBGYN
Start here:
Internal Medicine, Cardiology, Oncology, Neurology
Start here:
Emergency Medicine, Critical Care, Pulmonary Medicine
Start here:
Pediatrics, Neonatology, Child Health
Start here:
The handbook helps physicians critically evaluate AI tools, integrate them responsibly into clinical workflows, understand their limitations, and determine which applications have credible evidence of improving care or workflow.
Three approaches are available: use chapter TL;DRs for a quick scan, read full chapters for implementation planning, or combine the Foundations, relevant specialty, and Implementation chapters for a specialty-focused path.
The handbook prioritizes peer-reviewed research, official regulatory records, professional-society guidance, and documented real-world implementations. Vendor materials and press releases are labeled as such and do not substitute for independent clinical evidence.
Bryan Tegomoh, MD, MPH
Berkeley, California
October 2025
Bryan Tegomoh, MD, MPH, is a physician and medical epidemiologist whose work spans pathogen surveillance, pandemic preparedness, and clinical AI evaluation. He helped establish Nebraska’s statewide pathogen genomic surveillance program through CDC and CSTE, served as a technical consultant to the World Health Organization on epidemic and pandemic preparedness from 2022 to 2024, and contributed to clinical evaluations of AI models at xAI. He is Deputy Editor of Rapid Reviews: Infectious Diseases, and his work has been published in Science, Nature Methods, Nature Microbiology, and MMWR.