Artificial Intelligence in Medicine for Clinical Practice : Applying AI to Diagnosis, Risk Prediction, Treatment Planning, Documentation, Monitoring, and Patient Safety. Este artículo no está disponible.Artificial Intelligence in Medicine for Clinical Practice : Applying AI to Diagnosis, Risk Prediction, Treatment Planning, Documentation, Monitoring, and Patient Safety

An AI system can produce an answer in seconds.

The harder question is whether that answer deserves a place in the patient's care.

Artificial Intelligence in Medicine for Clinical Practice is written for the point where technology meets professional responsibility—the moment a clinician must decide whether an algorithmic recommendation is useful, incomplete, misleading, or potentially unsafe.

This is not a book about admiring artificial intelligence from a distance. It is about understanding how AI enters real clinical work: helping interpret diagnostic information, estimating risk, informing treatment choices, reducing documentation burden, following patients over time, and identifying patterns that may be difficult to detect through conventional workflows alone.

But speed is not the same as reliability.

A risk score can be impressive and still be poorly calibrated for the patient in front of you. A generated note can save time while quietly introducing an error. A prediction model can perform strongly in development yet weaken when moved into another population. And a useful decision-support system can become dangerous when its output is accepted without sufficient clinical scrutiny.

That is why this book keeps the focus where it belongs: AI as a clinical tool, not a substitute for clinical judgment.

Readers will discover how to:

• understand where machine learning and predictive systems can realistically support diagnosis and prognosis;
• examine how AI can contribute to treatment planning and clinical prioritization without turning algorithmic output into automatic authority;
• use technology more intelligently in documentation and information-heavy workflows;
• interpret the role of continuous data, digital biomarkers, and automated systems in patient monitoring;
• recognize problems involving bias, poor data quality, weak generalizability, automation error, and misplaced confidence;
• evaluate AI output with greater attention to validation, context, human oversight, and patient safety;
• connect emerging technology to the practical realities of clinical responsibility, workflow, communication, and follow-up.

The value of AI in medicine does not come from using more technology.

It comes from knowing which technology is appropriate, what question it is actually answering, what evidence supports it, what it may be missing, and when the clinician must override it.

That makes this book particularly useful for physicians, residents, medical students, advanced-practice clinicians, clinical educators, healthcare leaders, and professionals who want to understand AI without having to become software engineers first.

Current medical-AI research reinforces the need for this balanced approach: high performance on diagnostic or workflow measures does not automatically prove improved patient outcomes, and questions of data quality, transparency, generalizability, and clinical impact remain central. Wiley Online Library

Artificial Intelligence in Medicine for Clinical Practice gives readers a practical framework for working with intelligent systems while keeping clinical reasoning, accountability, and patient welfare at the center of every decision.

If AI is becoming part of your clinical environment, make sure understanding it becomes part of your professional toolkit. Add this book to your medical library and learn how to use emerging technology with greater judgment, scrutiny, and confidence.