Trustworthy Medical AI: A Builder's Guide to Safe, Compliant Software as a Medical Device - Softcover

Inhaltsangabe

The model is never the problem. The architecture around it is.

Clinical AI is entering hospitals faster than the safeguards meant to keep it trustworthy. Systems that pass validation drift silently in the field. Alerts that cry wolf get switched off. A single unverified output compounds into a decision no one can trace back. The line between AI that helps and AI that harms isn't the accuracy of the model — it's the discipline of the loop built around it.

Trustworthy Medical AI is a practical, story-driven guide to building software as a medical device that clinicians can trust and regulators can clear. Written by a former Technical and Clinical Assessor at a Notified Body who also built a regulated surgical AI platform through multicenter clinical validation, it turns hard-won, two-sided experience into a clear engineering discipline.

Inside, you'll learn how to:

• Design the five-stage feedback loop — Define, Build, Verify, Deploy, Learn — that keeps a human meaningfully in command
• Map your architecture cleanly onto IEC 62304 and ISO 14971, so compliance becomes a byproduct of good engineering, not a scramble before submission
• Satisfy the FDA's Total Product Life Cycle approach and the EU AI Act's human-oversight requirements
• Recognize the failure patterns — validation gaps, silent drift, alert fatigue — before they reach a patient
• Prepare for the generative and agentic AI frontier, where the loop matters more, not less

Every claim is grounded in publicly available evidence and cited — from landmark studies on automation bias and model drift to real, FDA-cleared deployments and well-documented failures.

This book is for: MedTech engineers and founders, regulatory affairs professionals, clinical informaticists, and anyone responsible for AI that a clinician — and a patient — must be able to trust.

How do you know it keeps working safely once it leaves your lab? This book is how you find out before it's too late to matter.

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