Handbook of Artificial Intelligence in Medicine: A Textbook Covering Machine Learning, Deep Learning

That gap - between the AI tools being deployed in your hospital and the clinical knowledge required to use them safely, question them critically, and protect the patients they miss - is where avoidable harm accumulates.

This book closes that gap, giving clinicians the evidence, the frameworks, and the specific governance vocabulary to practise with authority in an AI-integrated healthcare system.

- AI does not replace your judgment - it redirects where your judgment must focus, and this book shows you exactly where that boundary sits across every major specialty.
- From ECG interpretation to genomic profiling to sepsis prediction - each AI application is examined with its actual validation evidence, its documented failure modes, and the patient subgroups most at risk when the model underperforms.
- Racial and demographic bias in clinical AI is not a policy concern - it is a bedside problem, and the sepsis model case study shows you how to detect it, respond to it, and report it before patients are harmed.
- The regulatory pathways, FDA clearance standards, and EU AI Act obligations are translated into the clinical procurement questions every institution must ask before switching on a new AI tool.
- Implementation science, change management, shadow deployment, and post-market monitoring are treated as patient safety disciplines, not IT project management.
- The closing chapters address what AI genuinely cannot do - and why the therapeutic relationship, moral judgment, and clinical advocacy are the professional investments that matter most now.
Written for physicians, nurses, pharmacists, residents, and all clinicians whose patients will be affected by AI in the next decade of their practice.

Add this to your cart as a commitment to understanding the tools that are already shaping your patients' outcomes.