Full Description
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.
Contents
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
