Smart Healthcare 2.0

Integrating Digital Twins with AI-Driven Predictive Analytics

Ramesh Chandra Poonia, Kamal Upreti (Herausgeber)

Buch | Softcover

250 Seiten

2026
Academic Press Inc (Verlag)
978-0-443-45282-6 (ISBN)

Infos

Smart Healthcare 2.0: Integrating Digital Twins with AI-Driven Predictive Analytics offers a ground-breaking 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. Each chapter is complemented by pedagogical tools and supported by a companion website offering extended resources for teaching and applied research. Researchers and academics will find a consolidated, interdisciplinary framework linking theory with practical healthcare applications, ideal for advancing scholarship and innovation.

Biomedical and clinical engineers gain actionable insights into system design, sensor integration, and clinical validation for building reliable, patient-centered solutions. Healthcare AI engineers and data scientists will benefit from specialized guidance on deploying predictive models, managing multi-sensor data fusion, and ensuring privacy-compliant, real-time analytics. This book empowers stakeholders across the healthcare ecosystem to drive the next generation of intelligent, adaptive, and trustworthy digital health systems.

Autor

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, Ålesund, 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. Dr. Kamal Upreti is an Associate Professor in the Department of Computer Science at CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India. He holds a B.Tech (Hons) from UPTU, an M.Tech (Gold Medalist), a PGDM (Executive) from IMT Ghaziabad, a Ph.D. in Computer Science & Engineering, and completed a postdoc at National Taipei University of Business, Taiwan, funded by MHRD. With over 15 years of teaching, research, and corporate experience, Dr. Upreti has published 50+ patents, 32 magazine issues, 110+ research papers, and authored or edited 45+ books with publishers like CRC Press and Oxford. His expertise spans modern physics, data analytics, cybersecurity, machine learning, healthcare, embedded systems, and cloud computing. He has worked with organizations including HCL, NECHCL, Hindustan Times, and various academic institutes. Notable projects include Japan’s “Hydrastore,” India’s Integrated Power Development Scheme (IPDS), and a significant ICMR-funded cardiovascular disease prediction project (₹80 Lakhs) in collaboration with GB Pant and AIIMS Delhi. He has secured funding from DST SERB (₹5 Lakhs) for ICSCPS-2024 and AICTE-IBIP (₹10 Lakhs) for 2024-2026. Dr. Upreti frequently serves as a session chair, keynote speaker, corporate trainer, and faculty developer. He has been honored as Best Teacher, Best Researcher, and Gold Medalist in his M.Tech program.

Inhalt

1. Digital Twins in Precision Health: From Static Models to Adaptive Virtual Patients
2. 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

Produkt-Details

• Erscheint lt. Verlag: 1.6.2026
• Reihe/Serie: Advances in ubiquitous sensing applications for healthcare
• Verlagsort: San Diego
• Sprache: englisch
• Maße: 191 x 235 mm
• Gewicht: 450 g
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie
• Themenwelt: Technik ► Medizintechnik
• ISBN-10: 0-443-45282-2 / 0443452822
• ISBN-13: 978-0-443-45282-6 / 9780443452826
• Zustand: Neuware