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

· · · ·
· Academic Press
Ebook
400
Pages
Eligible
This book will become available on March 1, 2027. You will not be charged until it is released.

About this ebook

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. - Enhances understanding of autonomous AI systems for improved diagnostic accuracy and personalized treatment optimization in healthcare - Provides practical insights into integrating adaptive AI for streamlined hospital workflows and real-time patient monitoring - Addresses ethical, regulatory, and privacy challenges critical to deploying AI in sensitive medical environments - Offers interdisciplinary perspectives that benefit students, researchers, and healthcare professionals seeking advanced AI applications - Explores cutting-edge AI techniques for accelerating drug discovery, rehabilitation, and autonomous medical robotics
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 n...

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About the author

Dr. Rajesh Kumar Dhanaraj is a professor at the Symbiosis International (Deemed University) in Pune, India. His research and publication interests include cyber-physical systems, wireless sensor networks, and cloud computing. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the Computer Science Teacher Association (CSTA) and member of the International Association of Engineers (IAENG). He is an expert advisory panel member of Texas Instruments Inc. (USA), and an associate editor of International Journal of Pervasive Computing and Communications (Emerald Publishing).

Dr. M. Sangeetha is an Assistant Professor (SLG) in the Department of Computer Science and Engineering, PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu, India. She received her M.E. degree in Computer Science and Engineering from Anna University, India, in 2010. She completed her Ph.D. in Information and Communication Engineering from Anna University, Chennai, India, in 2024. She is a life member of the Computer Society of India (CSI). Dr. Sangeetha has published more than 20 research papers in high-quality SCI impact factor journals indexed in Scopus and ESCI, 24 papers in international conferences indexed by Springer and IEEE Xplore, and holds one patent.

Dr. R. Manjula Devi received her B.E. and M.E. degrees in Computer Science and Engineering in 2004 and 2006, respectively, from Bharathiyar University, Coimbatore, and Anna University, Chennai. She earned her Ph.D. degree in Information and Communication Engineering, specializing in Neural Networks, from Anna University, Chennai, in 2015. She is currently a Professor in the Department of Computer Science and Engineering at KPR Institute of Engineering and Technology, Coimbatore. She has received numerous prestigious awards, including the Best Faculty Award in CSE, Shri P.K. Das Memorial Best Faculty Award, Best Author Award, Best Young Teacher Award, and the Dr. A.P.J. Abdul Kalam Award, among others, from various organizations. She is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and a member of the Computer Society of India. She also serves as a reviewer for various journals and conferences. Her research interests include Soft Computing, Pattern Classification and Recognition, Internet of Things, and Image Processing.

Mahmoud Ahmad Al-Khasawneh is a faculty member in the School of Computing Skyline University College, Sharjah UAE. His scholarly pursuits span a diverse array of fields within computer science. He has authored numerous papers in esteemed, peer-reviewed journals across leading publishers such as IEEE, Springer, Wiley, Hindawi, and MDPI. His research interests encompass Security, Image Encryption, Wireless Networks, Blockchain, Internet of Things, and Big Data. With a commitment to advancing knowledge and solving contemporary challenges in these domains, he actively engages in research, teaching, and mentorship, contributing to the academic and professional development of his students and peers. Driven by a passion for innovation and a dedication to excellence, he continues to make significant contributions to the field, shaping the future of technology and its applications.

Dr. Firoz Khan is an Assistant Professor at Ball State University’s Center for Information and Communication Sciences. His research focuses on securing systems under attack, with particular emphasis on network security, information assurance, and cybersecurity. He also investigates the application of machine learning techniques for data analysis and big data challenges. Dr. Khan is dedicated to educating students in computer and network security, ethical hacking, digital forensics, and the integration of machine learning with cybersecurity issues. His scholarly work has been widely recognized, with over 850 citations highlighting his significant impact in the field.

Dr. Rajesh Kumar Dhanaraj is a professor at the Symbiosis International (Deemed University) in Pune, India. His research and publication interests include cyber-physical systems, wireless sensor networks, and cloud computing. He is a senior member of the Institute of Electrical and Electronics En...

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