Healthcare 5.0

Applications of Artificial Intelligence, Machine Learning, Iomt, and Big Data
Erscheinungsjahr 2026
ISBN: 978-0-443-44799-0
Verlag: Elsevier Science

Buch, Englisch, 320 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 449 g

Healthcare 5.0: Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data addresses the urgent need for innovation in today’s complex healthcare data landscape, characterized by pandemics, aging populations, and escalating chronic conditions. This book introduces the concept of ‘Healthcare 5.0’ as an interconnected, data-driven, and patient-centric framework, where advanced technologies—such as AI, ML, IoMT, Big Data, and Large Language Models (LLMs)—converge to optimize care, streamline operations, and deliver personalized, predictive solutions that meet real-world challenges. Comprising six comprehensive sections, the book moves from core AI applications in electronic health records, drug discovery, data management, and privacy, through cutting-edge big data analytics for precise disease forecasting and diagnosis. It explores new research advances in the Internet of Medical Things including connected device architectures and their fusion with AI for dynamic decision-making. The third section focuses on data analytics in telemedicine, remote care, system usability, and integration in Healthcare 5.0. The personalized healthcare section details analysis and applications in AI- and IoT-powered assistance, and real-time monitoring. The last section explores the development of LLMs and their applications in medical imaging, clinical decision support, predictive analytics, system architectures, as well as the ethical challenges of their deployment in healthcare. Healthcare 5.0: Applications of Artificial Intelligence, Machine Learning, IoMT, and Big Data serves as an essential resource for graduate students, researchers, and engineers in computer science, data science, and biomedical informatics. It bridges theory and practical application, offering interdisciplinary insights, foundational background, detailed case studies, and guidance on navigating the next generation of healthcare data systems. Whether for research or real-world innovation, readers gain the tools to design, analyze, and implement intelligent healthcare data solutions for a rapidly evolving digital era.

Autoren/Hrsg.

Kumar, Yugal

Kumar, Pardeep

Dong, Ming

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme

Inhaltsverzeichnis

Part I. Artificial Intelligence (AI) and Machine Learning (ML) for Healthcare 5.0
1. AI-Enabled Electronic Health Records (EHR): Transforming Patient Data Management
2. Role of AI and ML in Drug Discovery and Development: A Design Perspective Approach
3. Data Privacy and Security in AI-Driven Healthcare Systems
4. Future of AI in Healthcare: Trends and Emerging Technologies

Part II. Role of Big Data Analytics in Disease Diagnosis and Prediction
5. Accurate Disease Diagnosis and Predictive Healthcare: Dig Data Perspective
6. Integrating Big Data Analytics for Precision Medicine and Disease Forecasting
7. Big Data Strategies in Clinical Diagnosis and Predictive Medicine
8. Big Data in Disease Diagnosis: From Raw Data to Predictive Insights

Part III. Internet of Medical Things (IoMT) for Innovative Healthcare System
9. IoMT for Elderly Care: Enhancing Quality of Life and Independence
10. Design an Effective and Reliable Architecture of IoMT for Connected Healthcare
11. Harnessing Insights from Medical Devices in IoMT: A Data Management and Analytics Approach
12. Integration of IoMT and Artificial Intelligence for Enhancing Decision-Making in Healthcare
13. Next-Generation IoMT: Trends and Future Directions in Connected Healthcare

Part IV. Usability and Significance of Telemedicine and Remote Care Healthcare 5.0
14. Telemedicine in Healthcare 5.0: Usability and Impact on Modern Healthcare
15. Enhancing Remote Care through Telemedicine: Usability and Integration in Healthcare 5.0
16. Role of AI in Telemedicine: Enhancing Remote Care with Smart Technologies
17. Future of Telemedicine in Healthcare 5.0: Usability, Challenges, and Opportunities

Part V. Personalized HealthCare Assisted System
18. Design and Development of a Personalized Healthcare Assistance System Using AI and IoT
19. AI-Enabled Personalized Healthcare Assistance: A Framework for Precision Medicine
20. A Comprehensive Review of Personalized Healthcare Assistance Systems and Their Applications
21. Real-Time Monitoring and Decision Support in Personalized Healthcare Assistance Systems

Part VI. Foundations of Large Language Models in Healthcare
22. Explore capability of the LLMs in Medical Image Analysis and Diagnostics
23. Architectures of LLMs for Healthcare and IoT-Based Applications
24. Challenges and Opportunities in Deploying LLMs in IoT and Healthcare
25. Personalized Treatment Planning with AI-Driven Language Models
26. Ethical Considerations, Bias, and Challenges of LLMs in Medical Applications
27. LLMs for Clinical Decision Support and Evidence-Based Medicine
28. LLMs for Predictive Maintenance and Fault Detection in Medical IoT Systems

Über Autor(innen)

Kumar, Yugal
Dr. Kumar is an Associate Professor in the Department of Computer Engineering, School of Technology Management and Engineering, NMIMS University, Chandigarh Campus, Mumbai, India. Prior to joining NMIMS University, Dr. Kumar was associated with Jaypee University of Information Technology (JUIT), Wakanaghat, Himachal Pradesh, India. He completed his PhD in Computer Science & Engineering from Birla institute of Technology, Mesra, Ranchi. He has more than 17 years of teaching and research experience, has published over 120 research papers in reputed journals, edited more than eight books, and has presented at various national and international conferences. His primary area of research includes medical informatics, meta-heuristic algorithms, data clustering, swarm intelligence, pattern recognition, medical data analytics.

Dong, Ming
Ming Dong is currently a full professor and the Associate Chair in the Department of Computer Science and the co-director of the Data Science and Business Analytics MS program and the AI, Big Data & Analytics Group at Wayne State University. He is also the director of the Machine Vision and Pattern Recognition Lab.

Dr. Dong's areas of research include deep learning, data mining, and computer vision with applications in health informatics and automotive industry. His research is funded by National Science Foundation, National Institutes of Health, State of Michigan, Private Foundations (e.g., Michigan Health Endorsement Fund, Epilepsy Foundation) and Industries (e.g., APB Investment, Ford Motor Company). He has published over 100 technical articles in premium journals and conferences in related fields, e.g., TMI, TMM, TPAMI, TKDE, TNN, TVCG, TC, IEEE CVPR, IEEE ICCV, IEEE ICDM, ACM MM, MICCAI, AMIA and WWW. He is/was an associate editor of Statistical Analysis and Data Mining, the American Statistical Association (ASA) Data Science Journal (since 2018), Journal of Smart Health (Since 2016), IEEE Transactions on Neural Networks (2008-2011), and Pattern Analysis and Applications (2007-2010), and served in many conference program committees and US National Science Foundation panels. He also served as senior research consultant in Baidu Inc. in 2008.

Kumar, Pardeep
Dr. Pardeep Kumar is a Professor in the Department of Computer Science & Engineering at Jaypee University of Information Technology (JUIT), Wakanaghat. With more than 17 years of extensive experience in higher education, Dr. Kumar has served as Executive General Chair of 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) and 2024 Eighth International Conference on Parallel, Distributed and Grid Computing (PDGC), Guest Editor of Special Issue on "Robust and Secure Data Hiding Techniques for Telemedicine Applications", Multimedia Tools and Applications: An International Journal, Lead Guest Editor of Special Issue on "Recent Developments in Parallel, Distributed and Grid Computing for Big Data", published in International Journal of Grid and Utility Computing, Guest Editor of Special Issue on "Advanced Techniques in Multimedia Watermarking", published in International Journal of Information and Computer Security. Dr. Kumar is an Associate Editor of IEEE Access Journal. Dr. Kumar’s research focus includes machine & deep learning optimized Internet of Things (IOT) solutions to real life complex problems; blockchain, Internet of Things, data science and artificial intelligence for smart cities including AI driven health and medical informatics, big data analytics.