Preface
AI as a Clinical Tool, Not a Revolution
Artificial intelligence is entering clinical medicine not as a revolution that will replace physicians, but as a new category of tool requiring the same critical evaluation we apply to any clinical intervention.
Algorithms interpret chest X-rays. Deep learning models analyze pathology slides. Natural language processing extracts insights from electronic health records. But here’s what you rarely hear: Most of these tools aren’t as good as advertised. Many fail in real clinical environments. Some have been withdrawn after causing harm. Even successful ones require careful implementation and honest acknowledgment of limitations.
This handbook addresses the challenge physicians face: How do we critically evaluate these tools, integrate them responsibly into clinical workflow, understand their limitations, and decide which applications genuinely improve patient care?
Handbook Scope and Purpose
This is a field guide for evidence-based AI evaluation, not a computer science textbook. If you want to build neural networks from scratch, excellent technical resources exist elsewhere.
This handbook prioritizes peer-reviewed evidence from major medical journals, FDA-cleared applications, and real-world implementations over press releases and vendor whitepapers. Where evidence clearly supports specific tools, I name them. Where tools have failed despite marketing hype, I document those failures. Physicians deserve honest assessments, not diplomatic neutrality between good and bad applications.
Reading Guide
You don’t need to read sequentially. Jump directly to your specialty chapter. Use the search function. Read the TL;DR summaries for quick orientation. Dive deep when evaluating tools for your practice.
Three approaches:
- Quick Scan: Read chapter TL;DRs only for rapid orientation
- Deep Dive: Full chapters for implementation planning
- Specialty Focus: Part I (Foundations) + your specialty + Part III (Implementation)
Every chapter begins with an expandable Chapter Summary (TL;DR) containing clinical context, key evidence, what works, what doesn’t, and the clinical bottom line. Many physicians use TL;DRs for 80% of their needs and read full chapters only when implementing specific tools.
Stay skeptical. Demand prospective clinical trials, not just retrospective validation studies. You remain legally and ethically responsible for clinical decisions, regardless of what any algorithm recommends.