Smart Healthcare 2.0: Integrating Digital Twins with AI-Driven Predictive Analytics offers a groundbreaking exploration of how digital twin technology, combined with real-time sensing and predictive analytics, is transforming healthcare delivery. As the global healthcare landscape shifts toward proactive, personalized care, this book addresses the urgent need for comprehensive resources that unify artificial intelligence, Internet of Things (IoT), and biomedical engineering within the digital twin framework. It provides an essential guide for researchers, engineers, and clinicians aiming to harness virtual patient models and data-driven insights to improve health outcomes and system efficiency in the era of ubiquitous healthcare.
This volume covers a wide spectrum of topics, starting with foundational concepts of digital twins in precision health and advancing through smart sensing technologies, scalable system architectures, and AI-powered predictive analytics. Readers will explore detailed discussions on edge-cloud computing, secure communication protocols including blockchain, and simulation platforms that enable virtual patient modeling. The book also addresses critical themes such as chronic disease management, emergency response optimization, ethical AI deployment, interoperability standards, and workforce readiness. Real-world case studies and future-focused chapters on cognitive twins and quantum simulation provide a rich, multidisciplinary perspective.
This volume covers a wide spectrum of topics, starting with foundational concepts of digital twins in precision health and advancing through smart sensing technologies, scalable system architectures, and AI-powered predictive analytics. Readers will explore detailed discussions on edge-cloud computing, secure communication protocols including blockchain, and simulation platforms that enable virtual patient modeling. The book also addresses critical themes such as chronic disease management, emergency response optimization, ethical AI deployment, interoperability standards, and workforce readiness. Real-world case studies and future-focused chapters on cognitive twins and quantum simulation provide a rich, multidisciplinary perspective.
Table of Contents
1. Digital Twins in Precision Health: From Static Models to Adaptive Virtual Patients2. Ubiquitous Healthcare 3.0: Principles, Paradigms, and Proactive System Design
3. Smart Sensing in Digital Health: Wearable and Implantable Technologies
4. Architecture 3.0 for Digital Twin-Driven U-Healthcare Systems
5. Predictive Analytics in Health: Models and AI-Powered Applications
6. Edge-Fog-Cloud Continuum for Scalable Digital Twin Computation
7. Data Fusion and Context Awareness in Digital Twin Systems
8. Secure Communication 3.0 and Blockchain for Trustworthy Digital Health
9. Simulation Platforms for Virtual Patients: Modeling, Testing, and Visualization
10. Chronic Disease Management 3.0: Twin-Based Continuous Monitoring and Intervention
11. Emergency Response Systems Powered by Predictive Digital Twins
12. Integration with EHR and Smart Hospital Systems
13. Ethical AI in Healthcare Twins: Privacy, Regulation, and Fairness
14. Evaluation and Validation Metrics for Healthcare Digital Twins
15. Interoperability Standards and Open Frameworks for Digital Health Ecosystems
16. Global Case Studies: Twin Deployments Across HealthTech Ecosystems
17. Future Horizons 4.0: Cognitive Twins, Federated Intelligence, and Quantum Simulation
18. Healthcare Workforce Readiness and Training
19. Eco-Sustainability and Green Computing in Smart Healthcare
20. Legal and Regulatory Compliance in Digital Twin-Enabled Healthcare
Authors
Ramesh Chandra Poonia Professor, Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, India. Dr. Ramesh Chandra Poonia is a Professor at the Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India. Recently completed his Postdoctoral Fellowship from CPS Lab, Department of ICT and Natural Sciences, Norwegian University of Science and Technology, �lesund, Norway. He has received his Ph.D. degree in Computer Science from Banasthali University, Banasthali, India in July 2013. His research interests are Cyber-Physical Systems, Network Protocol Evaluation and Artificial Intelligence. He is Chief Editor of TARU Journal of Sustainable Technologies and Computing (TJSTC) and Associate Editor of the Journal of Sustainable Computing: Informatics and Systems, Elsevier. He also serves in the editorial boards of a few international journals. He is main author and co-author of 06 books and an editor of more than 25 special issue of journals and books including Springer, CRC Press - Taylor and Francis, IGI Global and Elsevier, edited books and Springer conference proceedings and has authored/co-authored over 65 research publications in peer-reviewed reputed journals, book chapters and conference proceedings. Kamal Upreti Associate Professor, Department of Computer Science, CHRIST(Deemed to be University), Delhi-NCR Ghaziabad, Uttar Pradesh, India.Dr. Kamal Upreti is an Associate Professor of Computer Science at CHRIST (Deemed to be University), Ghaziabad. He holds , a Ph.D. in Computer Science & Engineering, and a postdoctoral fellowship at National Taipei University of Business, Taiwan, funded by MHRD.
With teaching, research, and industry exposure, he has produced numerous patents and publications. His interests span modern physics, data analytics, cybersecurity, ML, healthcare, embedded systems, and cloud computing. Notable projects include Hydrastore in Japan, IPDS in India, and an ICMR-funded cardiovascular-prediction project with GB Pant and AIIMS Delhi.
Dr. Upreti serves as session chair, keynote speaker, trainer, and faculty developer, and has been honored as Best Teacher, Best Researcher, and an M.Tech Gold Medalist.
