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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xxi 7. Preface - p. xxv 8. Section 1: Foundations of digital twins and AI in healthcare - p. S1 9. Chapter 1. Open frameworks for a connected digital health ecosystem: challenges and opportunities - p. 3 10. Chapter 2. Data fusion and context awareness in digital twin systems - p. 21 11. Chapter 3. Privacy in healthcare digital twins - p. 43 12. Chapter 4. Privacy-preserving smart contracts for healthcare: redefining secure communication frameworks - p. 63 13. Section 2: AI-driven predictive analytics & smart sensing - p. S83 14. Chapter 5. Integrating machine learning with electronic health records for smart hospital analytics: a cancer survival prediction case study - p. 85 15. Chapter 6. Smart wearables and implants in predictive healthcare: toward personalized and preventive medicine - p. 105 16. Chapter 7. Artificial intelligence-enabled patient interaction monitoring: a dashboard approach to safety, adherence, and clinical insights - p. 125 17. Chapter 8. Mitigating bias in artificial intelligence-driven burnout risk predictions for optimal staff scheduling in healthcare - p. 155 18. Section 3: Digital twin-based simulation & virtual care - p. S179 19. Chapter 9. Virtual patient simulation using digital twins for cardiovascular risk prediction and progression modeling - p. 181 20. Chapter 10. Augmented reality-virtual reality in remote health care: virtual diagnosis - p. 203 21. Section 4: Ethics, governance, and compliance - p. S223 22. Chapter 11. Ethical governance of artificial intelligence-driven predictive analytics in smart healthcare - p. 225 23. Chapter 12. Human-centered ethics for artificial intelligence-integrated digital health ecosystems - p. 247 24. Chapter 13. Legal and regulatory compliance in digital twin-enabled healthcare - p. 269 25. Section 5: Workforce, sustainability, and global adoption - p. S289 26. Chapter 14. Healthcare workforce readiness and training - p. 291 27. Chapter 15. Eco-sustainability and green computing in smart healthcare - p. 309 28. Chapter 16. Global case studies: twin deployments across HealthTech ecosystems - p. 327 29. Section 6: Emerging frontiers & future horizons - p. S349 30. Chapter 17. Psychological dimensions of gift-giving and receiving in smart healthcare: enhancing happiness through artificial intelligence-based predictive analysis - p. 351 31. Chapter 18. Integrating bio-inspired intelligence with digital twins: a roadmap for smart healthcare 2.0 - p. 367 32. Chapter 19. Future Horizons 4.0: cognitive twins, federated intelligence, and quantum simulation - p. 389 33. Index - p. 407
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xxi 7. Preface - p. xxv 8. Section 1: Foundations of digital twins and AI in healthcare - p. S1 9. Chapter 1. Open frameworks for a connected digital health ecosystem: challenges and opportunities - p. 3 10. Chapter 2. Data fusion and context awareness in digital twin systems - p. 21 11. Chapter 3. Privacy in healthcare digital twins - p. 43 12. Chapter 4. Privacy-preserving smart contracts for healthcare: redefining secure communication frameworks - p. 63 13. Section 2: AI-driven predictive analytics & smart sensing - p. S83 14. Chapter 5. Integrating machine learning with electronic health records for smart hospital analytics: a cancer survival prediction case study - p. 85 15. Chapter 6. Smart wearables and implants in predictive healthcare: toward personalized and preventive medicine - p. 105 16. Chapter 7. Artificial intelligence-enabled patient interaction monitoring: a dashboard approach to safety, adherence, and clinical insights - p. 125 17. Chapter 8. Mitigating bias in artificial intelligence-driven burnout risk predictions for optimal staff scheduling in healthcare - p. 155 18. Section 3: Digital twin-based simulation & virtual care - p. S179 19. Chapter 9. Virtual patient simulation using digital twins for cardiovascular risk prediction and progression modeling - p. 181 20. Chapter 10. Augmented reality-virtual reality in remote health care: virtual diagnosis - p. 203 21. Section 4: Ethics, governance, and compliance - p. S223 22. Chapter 11. Ethical governance of artificial intelligence-driven predictive analytics in smart healthcare - p. 225 23. Chapter 12. Human-centered ethics for artificial intelligence-integrated digital health ecosystems - p. 247 24. Chapter 13. Legal and regulatory compliance in digital twin-enabled healthcare - p. 269 25. Section 5: Workforce, sustainability, and global adoption - p. S289 26. Chapter 14. Healthcare workforce readiness and training - p. 291 27. Chapter 15. Eco-sustainability and green computing in smart healthcare - p. 309 28. Chapter 16. Global case studies: twin deployments across HealthTech ecosystems - p. 327 29. Section 6: Emerging frontiers & future horizons - p. S349 30. Chapter 17. Psychological dimensions of gift-giving and receiving in smart healthcare: enhancing happiness through artificial intelligence-based predictive analysis - p. 351 31. Chapter 18. Integrating bio-inspired intelligence with digital twins: a roadmap for smart healthcare 2.0 - p. 367 32. Chapter 19. Future Horizons 4.0: cognitive twins, federated intelligence, and quantum simulation - p. 389 33. Index - p. 407
