The Physician AI Handbook

A Practical Guide for Clinicians Across All Specialties

Evidence-based guidance for physicians on evaluating, implementing, and optimizing AI tools across all medical specialties. 30 comprehensive chapters with 82+ peer-reviewed citations from JAMA, NEJM, Lancet.

Author

Bryan Tegomoh, MD, MPH

Published

November 2025

Built for Busy Clinicians

No programming background required • 15-20 min per chapter • Specialty-specific examples

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Evidence-Based

Citations from JAMA, NEJM, Lancet, Nature Medicine

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Clinical Focus

Real-world applications across 12+ specialties

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Honest Assessment

No vendor hype—just evidence and limitations

Why this handbook exists: As a physician who transitioned to medical informatics and epidemiology, I witnessed the gap between AI’s promise and its practical application. Research papers tout impressive metrics. Vendors promise revolutionary improvements. Yet physicians face critical questions: Which AI tools actually work? How do I evaluate vendor claims? What are the medico-legal implications? This handbook bridges that gap with curated, evidence-based guidance for every specialty.

Quick Start: Choose Your Path

Select the pathway that matches your specialty and immediate needs:

Primary Care & Family Medicine

“I need practical AI tools for my daily practice”

Start here (20 min): - AI Fundamentals - What AI actually is - Primary Care Applications - AI in family medicine - LLMs in Practice - ChatGPT, Claude for clinical tasks - AI Physician Toolkit - Essential tools

Diagnostic Specialties

“Radiology, Pathology, Dermatology, Ophthalmology”

Start here (30 min): - Diagnostic Imaging & Radiology - AI-assisted diagnostics - Pathology & Laboratory - Digital pathology AI - Dermatology & Ophthalmology - Computer vision applications - Evaluation Framework - Assessing AI diagnostic tools

Surgical Specialties

“General Surgery, Orthopedics, Neurosurgery, OBGYN”

Start here (25 min): - Surgery & Perioperative Care - AI in the OR - OBGYN Applications - Maternal-fetal medicine AI - Clinical Workflow Integration - Practical implementation - Medical Liability - Legal considerations

Medical Specialties

“Internal Medicine, Cardiology, Oncology, Neurology”

Start here (30 min): - Internal Medicine & Hospital Medicine - AI for hospitalists - Cardiology Applications - Cardiovascular AI - Oncology & Precision Medicine - AI-driven cancer care - Neurology Applications - Neurological AI tools

Emergency & Critical Care

“I work in fast-paced, high-stakes clinical environments”

Start here (25 min): - Emergency Medicine & Critical Care - AI for acute care - Clinical AI Safety - Risk management - Workflow Integration - Seamless adoption - Case Studies - Real implementations

Pediatrics & Neonatology

“I care for pediatric and newborn patients”

Start here (25 min): - Pediatrics & Neonatology - AI in child health - Medical Ethics & Equity - Special pediatric considerations - Privacy & HIPAA - Protecting pediatric data - Physician Toolkit - Practical tools

New to AI entirely? → Begin with the Preface then Chapter 1: AI in Medicine

What is this handbook?

The Physician AI Handbook is an open-source, evidence-based practical guide for understanding and applying artificial intelligence in clinical medicine—written by a physician (MD, MPH, UC Berkeley) for physicians.

This is NOT another hype-filled “AI will revolutionize everything” book.

This is a clinical field guide for:

Practicing physicians across all specialties who need to understand AI tools and their limitations
Residents and fellows preparing for AI-augmented medical practice
Medical students entering a healthcare landscape transformed by AI
Hospital administrators making informed decisions about AI adoption
Clinical researchers exploring AI applications in their fields

What makes this different?

What you’ll get:

Evidence-based guidance with citations from JAMA, NEJM, Lancet, Nature Medicine, BMJ
Real clinical case studies (successes and failures)
Specialty-specific applications across 12+ medical disciplines
Honest assessments of what AI can and cannot do
Practical implementation guidance for your clinical workflow
Medical-legal considerations and liability frameworks
Open access forever

What you won’t get:

Generic AI hype without clinical evidence
Oversimplified “AI will replace doctors” narratives
Ignoring the complexity of real patients
Vendor marketing disguised as education
Theoretical concepts without practical application
Paywalled content or hidden fees

Book Structure: Your Roadmap

Part I: Foundations

AI history in medicine, fundamentals, clinical data challenges

Chapters 1-3 2-3 hours 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

Part II: Clinical Specialties

AI applications across 12+ medical specialties

Chapters 4-15 8-10 hours Jump to your specialty

Key topics: Radiology, Internal Medicine, Surgery, Pediatrics, OBGYN, Emergency/Critical Care, Oncology, Cardiology, Neurology, Primary Care, Pathology, Dermatology/Ophthalmology

Includes: Specialty-specific tools, evidence-based applications, real case studies

Part III: Implementation & Evaluation

Clinical deployment, ethics, privacy, safety, liability

Chapters 16-21 4-5 hours Critical for implementation

Key topics: Evaluating AI tools, medical ethics & equity, HIPAA compliance, clinical AI safety, workflow integration, medical liability & malpractice

Part IV: Practical Tools

Hands-on guidance for AI in daily practice

Chapters 22-25 3-4 hours Immediately applicable

Includes: AI toolkit for physicians, LLMs in clinical practice (ChatGPT, Claude, Copilot), AI-assisted documentation (ambient scribes), clinical research with AI

Part V: The Future

Emerging technologies, policy, global health, future perspectives

Chapters 26-30 4-5 hours Forward-looking insights

Topics: Emerging AI technologies, global health equity, healthcare policy & governance, medical misinformation, the physician-AI partnership

How to use this handbook

Browse and Search

Browse chapters in the Table of Contents
Use the search box to find specific topics
Each chapter includes TL;DR summary
Click “copy” icons for code examples

Choose Your Path

For specialists → Jump to your specialty chapter
For generalists → Start with Primary Care & Practical Tools
For residents/students → Read sequentially Part I → V
For administrators → Focus on Implementation & Ethics

About the Author

Bryan Tegomoh, MD, MPH is a physician and epidemiologist with experience spanning clinical medicine, health informatics, and disease surveillance. He earned his medical degree and practiced clinical medicine before completing his MPH at the University of California, Berkeley School of Public Health, where he focused on epidemiology and health data science.

Recognizing the transformative potential of AI in medicine—and the critical need for evidence-based physician education—Bryan invested years reviewing medical literature, testing clinical AI tools, and synthesizing research from leading journals including JAMA, NEJM, The Lancet, Nature Medicine, and specialty-specific publications.

This handbook emerged from that synthesis work: translating technical AI research into practical clinical guidance, evaluating vendor claims against peer-reviewed evidence, and organizing scattered information into a comprehensive resource specifically designed for practicing physicians who need to understand AI capabilities, limitations, and real-world applications without becoming machine learning engineers.

Acknowledgements & Inspiration

This handbook draws inspiration from excellent clinical and technical resources including:

The Epidemiologist R Handbook by Applied Epi
Stanford’s AI in Healthcare research and education programs
Research published in JAMA, NEJM, The Lancet, Nature Medicine, BMJ, and specialty journals
The open-source medical informatics and clinical AI research communities
AI Global Health Blog for practical AI perspectives

Nearly everything valuable here builds on published research, clinical implementations, and the work of countless physicians, researchers, and informaticists advancing medical AI. My contribution is synthesis and translation—gathering evidence, testing tools, and organizing knowledge specifically for clinical audiences. Credit for insights belongs to those whose work I learned from; responsibility for errors is mine.

Contributing

This is a living handbook. Your contributions make it better:

Report Issues Submit on GitHub

Suggest Edits Click “Edit” on any page

Share Clinical Cases Email your examples

Support the Project Star us on GitHub

Specialty-specific feedback especially welcome: Practicing physicians know their field’s nuances best. If you see gaps, errors, or opportunities to improve specialty chapters, please contribute.

Terms of Use

License

This work is licensed under the MIT License. Free to use, share, and adapt with attribution.

Citation

Tegomoh, Bryan. The Physician AI Handbook: A Practical Guide for Clinicians Across All Specialties. 2025. https://physicianaihandbook.com. Accessed [Date].

Academic & Clinical Use

Medical schools, residency programs, CME courses, and hospital training programs are welcome to use this material. Please cite appropriately and let us know how you’re using it!

Your Feedback Makes This Better

Found something inaccurate? Medicine moves fast, especially medical AI. Please submit an issue on GitHub

Have clinical experience with AI tools? Share your real-world insights

Working in a specialty not well-covered? Help us improve those chapters

Implemented AI in your practice? Tell your story—others can learn from your experience

Follow updates on Twitter/X

Ready to start?

New to AI? → Begin with the Preface then Chapter 1: AI in Medicine

Want specialty-specific guidance? → Jump to Part II: Clinical Specialties

Need practical tools now? → Check Chapter 22: AI Tools Every Physician Should Know

Evaluating a vendor’s AI product? → Read Chapter 16: Evaluating AI Clinical Decision Support