Metaverse and AI in Healthcare: A Federated Learning Approach addresses the transformative integration of artificial intelligence and metaverse technologies in healthcare. The book fills a critical gap by exploring how federated learning enables secure, decentralized data sharing and personalized medicine in virtual health platforms, meeting urgent demands for privacy, interoperability, and innovation. The book is structured into four parts covering foundational AI and federated learning concepts, augmented reality and metaverse applications, legal and cybersecurity challenges, and emerging strategic trends.
Contributors from academia and industry present chapters on predictive modeling, cybersecurity frameworks, AR fitness, legal perspectives, and AI-driven medical tourism which are supported by case studies and technical explanations. This reference equips graduate students, researchers, and professionals in academia and industry who specialize in computer science, federated learning, biomedical engineering, and digital healthcare with practical knowledge and forward-looking analysis.
1. Chapter 1 Introduction to the convergence of metaverse, artificial intelligence, and federated learning in healthcare - p. 3 2. Chapter 2 Exploring the potential of deep learning for transcription factor binding in deoxyribonucleic acid - p. 23 3. Chapter 3 Predictive modeling of Alzheimer’s disease using MRI images and machine learning algorithms - p. 37 4. Chapter 4 Heart disease prediction using rivalry-based ensemble learning: A weighted rivalry-based ensemble learning approach - p. 57 5. Chapter 5 Federated learning and machine learning for the detection of heart diseases - p. 71 6. Chapter 6 Predictive modeling for disease prevention - p. 85 7. Chapter 7 A resilient federated learning-based cybersecurity framework for healthcare systems - p. 105 8. Part 2: Metaverse and augmented reality in healthcare - p. P2 9. Chapter 8 Augmented reality fitness (ARFIT): Redefining fitness through immersive augmented reality experiences - p. 127 10. Chapter 9 Metasports in the Metaverse era: A new frontier for athlete performance and health - p. 143 11. Chapter 10 Augmented reality for pediatric rehabilitation: Legal considerations for disabled children in India - p. 165 12. Chapter 11 Metaverse-enabled digital twins: Building intelligent and ethical healthcare systems - p. 193 13. Part 3: Legal, ethical, and cybersecurity challenges - p. P3 14. Chapter 12 A critical evaluation of blockchain integration in smart healthcare system - p. 211 15. Chapter 13 Legal perspectives on cybersecurity for digital health platforms serving disabled children in India - p. 229 16. Chapter 14 Mitigating bias in AI-driven burnout risk predictions for optimal staff scheduling in healthcare - p. 251 17. Part 4: Strategic and emerging trends - p. P4 18. Chapter 15 Improving cardiac magnetic resonance imaging analysis through real-time object detection with YOLOv8 - p. 279 19. Chapter 16 Digital twin-driven cancer survival prediction: Machine learning and electronic health record (EHR) integration for smart hospitals - p. 301 20. Chapter 17 Chatgpt in medicine: Partnering with doctors for better healthcare - p. 323 21. Chapter 18 Artificial intelligence based medical tourism in 2024 and beyond: Emerging trends, challenges, and strategic imperatives - p. 351 22. Index - p. 379
Metaverse and AI in Healthcare: A Federated Learning Approach addresses the transformative integration of artificial intelligence and metaverse technologies in healthcare. The book fills a critical gap by exploring how federated learning enables secure, decentralized data sharing and personalized medicine in virtual health platforms, meeting urgent demands for privacy, interoperability, and innovation. The book is structured into four parts covering foundational AI and federated learning concepts, augmented reality and metaverse applications, legal and cybersecurity challenges, and emerging strategic trends.
Contributors from academia and industry present chapters on predictive modeling, cybersecurity frameworks, AR fitness, legal perspectives, and AI-driven medical tourism which are supported by case studies and technical explanations. This reference equips graduate students, researchers, and professionals in academia and industry who specialize in computer science, federated learning, biomedical engineering, and digital healthcare with practical knowledge and forward-looking analysis.
1. Chapter 1 Introduction to the convergence of metaverse, artificial intelligence, and federated learning in healthcare - p. 3 2. Chapter 2 Exploring the potential of deep learning for transcription factor binding in deoxyribonucleic acid - p. 23 3. Chapter 3 Predictive modeling of Alzheimer’s disease using MRI images and machine learning algorithms - p. 37 4. Chapter 4 Heart disease prediction using rivalry-based ensemble learning: A weighted rivalry-based ensemble learning approach - p. 57 5. Chapter 5 Federated learning and machine learning for the detection of heart diseases - p. 71 6. Chapter 6 Predictive modeling for disease prevention - p. 85 7. Chapter 7 A resilient federated learning-based cybersecurity framework for healthcare systems - p. 105 8. Part 2: Metaverse and augmented reality in healthcare - p. P2 9. Chapter 8 Augmented reality fitness (ARFIT): Redefining fitness through immersive augmented reality experiences - p. 127 10. Chapter 9 Metasports in the Metaverse era: A new frontier for athlete performance and health - p. 143 11. Chapter 10 Augmented reality for pediatric rehabilitation: Legal considerations for disabled children in India - p. 165 12. Chapter 11 Metaverse-enabled digital twins: Building intelligent and ethical healthcare systems - p. 193 13. Part 3: Legal, ethical, and cybersecurity challenges - p. P3 14. Chapter 12 A critical evaluation of blockchain integration in smart healthcare system - p. 211 15. Chapter 13 Legal perspectives on cybersecurity for digital health platforms serving disabled children in India - p. 229 16. Chapter 14 Mitigating bias in AI-driven burnout risk predictions for optimal staff scheduling in healthcare - p. 251 17. Part 4: Strategic and emerging trends - p. P4 18. Chapter 15 Improving cardiac magnetic resonance imaging analysis through real-time object detection with YOLOv8 - p. 279 19. Chapter 16 Digital twin-driven cancer survival prediction: Machine learning and electronic health record (EHR) integration for smart hospitals - p. 301 20. Chapter 17 Chatgpt in medicine: Partnering with doctors for better healthcare - p. 323 21. Chapter 18 Artificial intelligence based medical tourism in 2024 and beyond: Emerging trends, challenges, and strategic imperatives - p. 351 22. Index - p. 379
