Artificial Intelligence in Medicine 2026: Clinical Decision Support, Medical Imaging, Predictive Analytics, Generative Systems, and Responsible Deploy

By Caleb T. Winslow, Evan R. Markham, Lydia M. Serrano

Artificial intelligence is already changing how clinicians interpret images, estimate risk, document encounters, discover therapies, manage information, and organize patient care. The urgent question is no longer whether these systems will enter medicine, but how they can be evaluated, governed, and used safely.

ARTIFICIAL INTELLIGENCE IN MEDICINE 2026 provides a clinically grounded examination of the technologies reshaping modern healthcare. It connects computational principles with the realities of diagnosis, workflow, patient safety, regulation, ethics, and health-system implementation.

Presented in a professionally designed full-color edition, the book uses color-coded tables, clinical figures, visual frameworks, structured callouts, and practical reference tools to make complex technical and medical concepts easier to understand, compare, and apply.

Rather than presenting artificial intelligence as a collection of impressive demonstrations, this book examines what happens when algorithms enter real clinical environments. Readers learn how data quality, model design, human factors, validation, bias, monitoring, and accountability determine whether a system becomes useful, ineffective, or unsafe.

Inside, the book explores:

Machine-learning foundations and the medical data required to support reliable modelsClinical decision-support systems and the difference between useful assistance and disruptive alertingApplications in radiology, pathology, ophthalmology, cardiology, and other imaging-intensive specialtiesPredictive models for deterioration, readmission, resource use, and population-level riskNatural-language processing, ambient documentation, and large language modelsGenerative systems for clinical communication, synthetic data, education, and researchRobotics, automation, remote monitoring, and intelligent medical devicesDrug discovery, precision medicine, and computational therapeuticsAlgorithmic bias, fairness, explainability, privacy, cybersecurity, and patient trustClinical validation, implementation science, workflow redesign, and post-deployment monitoringRegulation, governance, procurement, workforce preparation, and organizational accountabilityPractical frameworks for determining whether an AI system is ready for real patient-care environments

The discussion moves beyond technical performance metrics. It explains why a model with strong laboratory results may still fail when introduced into clinical workflow, why human oversight must be deliberately designed rather than assumed, and why responsible deployment requires continuous evaluation after implementation.

Extensive full-color tables, clinical scenarios, governance frameworks, implementation tools, reference material, and practical appendices help readers translate complex concepts into structured decisions.

This book is designed for physicians, nurses, clinical informaticians, healthcare executives, medical educators, researchers, data scientists, technology developers, policymakers, and graduate students who need a rigorous understanding of both the opportunities and limitations of artificial intelligence in healthcare.

ARTIFICIAL INTELLIGENCE IN MEDICINE 2026 is a practical, full-color reference for professionals responsible for evaluating, selecting, implementing, supervising, governing, or studying intelligent systems in patient care.

• Format: Paperback
• Language: English
• ISBN: B0H947HS6R
• ISBN13: 9798187510894
• Release Date: July 2026
• Publisher: Independently Published
• Length: 438 Pages
• Weight: 2.22 lbs.
• Dimensions: 0.9" x 8.5" x 11.0"