Self-Learning AI in Healthcare

Self-Learning AI in Healthcare: Agentic Systems for Smarter Medicine

covers the transformative potential of advanced artificial intelligence within modern medicine.

The book addresses the critical need for self-learning and agentic AI systems that autonomously adapt, refine decision-making, and navigate complex clinical environments with minimal intervention.

Chapters provide foundational insights into the rise of self-learning AI and neural networks tailored for adaptive medical systems.

Subsequent chapters delve into unsupervised, semi-supervised, and reinforcement learning for autonomous healthcare decision-making, alongside decentralized edge AI approaches.

Specialized sections cover personalized medicine, hospital workflow optimization, remote patient monitoring, early disease detection, federated learning for privacy preservation, and more.

Further, this book explores AI applications in drug discovery, mental health support, radiology, digital twins, and medical robotics, culminating with an examination of future challenges, ethics, and regulatory frameworks shaping self-learning AI’s trajectory in healthcare.

It is tailored to serve a diverse yet specialized audience spanning academic, professional, and research sectors.

Healthcare IT professionals and clinical informatics specialists will gain practical guidance for implementing adaptive AI solutions within complex healthcare environments.

AI researchers and data scientists focused on developing self-learning models will find cutting-edge methodologies and case studies that advance medical applications.