Skip to main content
Elsevier

Self-Learning AI in Healthcare

Agentic Systems for Smarter Medicine

  • 1st Edition - October 1, 2026
  • Latest edition
  • Editors: Rajesh Kumar Dhanaraj, M. Sangeetha, R. Manjula Devi, Mahmoud Ahmad Al-Khasawneh, Firoz Khan
  • Language: English

Self-Learning AI in Healthcare: Agentic Systems for Smarter Medicine introduces an essential and timely exploration into the transformative potential of advanced artificial intell… Read more

Data Mining & ML

Unlock the cutting edge

Up to 20% on trusted resources. Build expertise with data mining, ML methods.

Explore now

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.

Key features

  • 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 benefiting students, researchers, and healthcare professionals seeking advanced AI applications
  • Explores cutting-edge AI techniques for accelerating drug discovery, rehabilitation, and autonomous medical robotics

Readership

Biomedical engineers interested in integrating autonomous AI systems into medical devices and healthcare workflows Healthcare IT professionals and clinical informatics specialists aiming to implement adaptive AI solutions in healthcare environments AI researchers and data scientists focused on developing self-learning models for medical applications Graduate and doctoral students in computer science, biomedical informatics, and health data science pursuing advanced AI knowledge

Table of 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

Product details

  • Edition: 1
  • Latest edition
  • Published: October 1, 2026
  • Language: English

About the editors

RD

Rajesh Kumar Dhanaraj

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).
Affiliations and expertise
Professor, Symbiosis International (Deemed University), Pune, India

MS

M. Sangeetha

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.

Affiliations and expertise
Department of Computer Science and Engineering, PSG Institute of Technology and Applied Research, Coimbatore, Tamil Nadu, India

RD

R. Manjula Devi

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.

Affiliations and expertise
Professor/ CSE, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

MA

Mahmoud Ahmad Al-Khasawneh

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.
Affiliations and expertise
Faculty, School of Computing Skyline University College, Sharjah UAE

FK

Firoz Khan

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.

Affiliations and expertise
Assistant Professor, Ball State University, Center for Information and Communication Sciences, Muncie, Indiana, USA