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Full Description
Self-Learning AI in Healthcare: Agentic Systems for Smarter Medicine introduces an essential and timely exploration into the transformative potential of advanced artificial intelligence within modern medicine. As healthcare faces mounting challenges—from managing vast, complex patient data to improving diagnostic precision and personalizing treatments—traditional AI models often fall short due to their static nature and dependence on human retraining. This 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. By bridging cutting-edge AI research with practical healthcare applications, it opens new pathways toward more intelligent, efficient, and responsive patient care. The book's comprehensive contents, contributed by leading global experts, span a wide range of pivotal topics. It begins with 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 AI-driven rehabilitation. 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. This book 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. Biomedical engineers seeking to integrate autonomous AI systems into medical devices and workflows will benefit from in-depth explorations of real-world innovations. Additionally, graduate and doctoral students in computer science, biomedical informatics, and health data science will acquire comprehensive knowledge essential for mastering the complexities of adaptive AI in healthcare.
Contents
1. The Rise of Self-Learning AI in Healthcare: A New Era of Intelligent Medicine
2. Neural Networks and Deep Learning for Self-Adaptive Medical Systems
3. Unsupervised and Semi-Supervised Learning for Medical Data Analysis
4. Reinforcement Learning for Autonomous Decision-Making in Healthcare
5. Edge AI and On-Device Learning for Decentralized Healthcare Systems
6. Personalized Medicine with Self-Learning AI for Treatment Optimization
7. Hospital Workflow Optimization with Self-Learning AI
8. Self-Learning Powered Remote Patient Monitoring and Real-Time Adaptation
9. Self-Learning AI for Early Disease Detection and Preventive Medicine
10. Federated Learning for Privacy-Preserving Self-Learning AI in Healthcare
11. AI-Driven Personalized Rehabilitation and Adaptive Therapy
12. Self-Learning AI for Drug Discovery and Development Acceleration
13. Self-Improving AI for Mental Health Support and Cognitive Therapy
14. Autonomous AI for Personalized Treatment Plans
15. Adaptive AI in Radiology: Real-Time Image Interpretation and Diagnosis
16. Digital Twins in Healthcare: Self-Learning AI for Predictive and Preventive Medicine
17. Self-Learning AI for Medical Robotics: Towards Autonomous Surgical and Assistive Systems
18. The Future of Self-Learning AI in Healthcare: Challenges, Ethics, and Regulatory Considerations
