Artificial Intelligence-Driven Metaverse Integration in Smart Healthcare
Advancing Personalized and Predictive Medical Capabilities
- 1st Edition - June 1, 2026
- Latest edition
- Editors: Joseph Bamidele Awotunde, Fuqian Shi, Subhendu Kumar Pani, Jing Shi
- Language: English
Artificial Intelligence-Driven Metaverse Integration in Smart Healthcare: Advancing Personalized and Predictive Medical Capabilities offers a comprehensive exploration of cuttin… Read more
Artificial Intelligence-Driven Metaverse Integration in Smart Healthcare: Advancing Personalized and Predictive Medical Capabilities offers a comprehensive exploration of cutting-edge technologies and their transformative potential in healthcare. The book delves into the application of artificial intelligence (AI) in the healthcare sector, examining machine learning algorithms, natural language processing, and computer vision to enhance healthcare delivery. It also introduces the metaverse, an immersive virtual environment, and explores its integration into medical practices, patient engagement, and health-related experiences. This is an invaluable resource for researchers and practitioners in biomedical fields, health informatics, and personalized and predictive medical systems.
In 26 chapters, the book discusses how the convergence of AI and the metaverse contributes to the development of smart healthcare systems that are more efficient, accessible, and personalized. It highlights AI's role in customizing medical treatments, diagnostics, and patient care, leading to more effective healthcare experiences. Additionally, it explores predictive analytics and modeling using AI to prevent diseases, optimize treatments, and improve healthcare outcomes. Providing insights into the current state of AI in healthcare and the potential of metaverse integration, the book includes case studies, practical applications, and future perspectives.
In 26 chapters, the book discusses how the convergence of AI and the metaverse contributes to the development of smart healthcare systems that are more efficient, accessible, and personalized. It highlights AI's role in customizing medical treatments, diagnostics, and patient care, leading to more effective healthcare experiences. Additionally, it explores predictive analytics and modeling using AI to prevent diseases, optimize treatments, and improve healthcare outcomes. Providing insights into the current state of AI in healthcare and the potential of metaverse integration, the book includes case studies, practical applications, and future perspectives.
- Explores the advancement of Artificial Intelligence in healthcare, its prospective for metaverse integration, and its potential to enhance personalized and predictive medical capabilities
- Gives insights on how artificial intelligence and the metaverse can be leveraged to provide more personalized healthcare solutions
- Addresses the technological hurdles, ethical considerations, and privacy concerns associated with integrating AI and the metaverse into healthcare
Researchers, academics and advanced level students in artificial intelligence and health care
Part I: Advanced Artificial Intelligence in Smart Healthcare Systems
1. Overview of AI-based in Smart Healthcare Systems
2. Artificial intelligence enabling Smart Healthcare Systems
3. Architectural Design Requirements for AI-based Smart Healthcare Systems
4. Computation Complexity and Energy-efficiency of AI-based Smart Healthcare Systems
5. Smart Healthcare big data analytics in the Medical industry
6. Systematic AI-Driven based Information System in Smart Healthcare
Part II: Applications of Metaverse in Smart Healthcare Systems
7. Metaverse Technologies in Healthcare Systems
8. Architectural Design Requirements for Metaverse-based Smart Healthcare Systems
9. Significance of Metaverse Integration in Smart Healthcare Systems
10. Virtual Reality (VR) and Augmented Reality (AR) Applications in Smart Healthcare Systems
11. Interoperability Standards for Metaverse Integration in Healthcare Systems
12. Forecasting Health Trends in the Metaverse
13. Energy-efficient Optimization Schemes for Metaverse-based Smart Healthcare Systems
14. Legal Framework and Regulatory Policies for Designing Metaverse-based Healthcare Systems
Part III: Advanced Personalized and Predictive Medicine using AI-Driven Metaverse
15. The Role of AI-Driven Metaverse Integration in Smart Healthcare
Part III Emerging Technologies in AI and Metaverse
16. Artificial intelligence enabling metaverse-based Healthcare Systems
17. Edge and cloud-based deployment of AI-Driven Metaverse Integration in Smart Healthcare
18. Blockchain-assisted computational tools for AI-Driven Metaverse Integration in Smart Healthcare
19. Ethical Considerations and Privacy in AI-Driven Metaverse Healthcare
20. Computation Complexity and Energy-efficiency of AI-Driven Metaverse Integration in Smart Healthcare
21. Security and Privacy Schemes for AI-Driven Metaverse Integration in Smart Healthcare
22. Threat assessment and mitigation techniques in AI-Driven Metaverse Integration in Smart Healthcare
23. Designing Intuitive Virtual Healthcare Interfaces using AI-Driven Metaverse
24. Successful Implementations of AI in Healthcare Metaverse
25. Industry Collaboration for Advancing AI-Driven Metaverse Integration in Smart Healthcare
26. Shaping the Future of Personalized and Predictive Healthcare using AI-Driven Metaverse
1. Overview of AI-based in Smart Healthcare Systems
2. Artificial intelligence enabling Smart Healthcare Systems
3. Architectural Design Requirements for AI-based Smart Healthcare Systems
4. Computation Complexity and Energy-efficiency of AI-based Smart Healthcare Systems
5. Smart Healthcare big data analytics in the Medical industry
6. Systematic AI-Driven based Information System in Smart Healthcare
Part II: Applications of Metaverse in Smart Healthcare Systems
7. Metaverse Technologies in Healthcare Systems
8. Architectural Design Requirements for Metaverse-based Smart Healthcare Systems
9. Significance of Metaverse Integration in Smart Healthcare Systems
10. Virtual Reality (VR) and Augmented Reality (AR) Applications in Smart Healthcare Systems
11. Interoperability Standards for Metaverse Integration in Healthcare Systems
12. Forecasting Health Trends in the Metaverse
13. Energy-efficient Optimization Schemes for Metaverse-based Smart Healthcare Systems
14. Legal Framework and Regulatory Policies for Designing Metaverse-based Healthcare Systems
Part III: Advanced Personalized and Predictive Medicine using AI-Driven Metaverse
15. The Role of AI-Driven Metaverse Integration in Smart Healthcare
Part III Emerging Technologies in AI and Metaverse
16. Artificial intelligence enabling metaverse-based Healthcare Systems
17. Edge and cloud-based deployment of AI-Driven Metaverse Integration in Smart Healthcare
18. Blockchain-assisted computational tools for AI-Driven Metaverse Integration in Smart Healthcare
19. Ethical Considerations and Privacy in AI-Driven Metaverse Healthcare
20. Computation Complexity and Energy-efficiency of AI-Driven Metaverse Integration in Smart Healthcare
21. Security and Privacy Schemes for AI-Driven Metaverse Integration in Smart Healthcare
22. Threat assessment and mitigation techniques in AI-Driven Metaverse Integration in Smart Healthcare
23. Designing Intuitive Virtual Healthcare Interfaces using AI-Driven Metaverse
24. Successful Implementations of AI in Healthcare Metaverse
25. Industry Collaboration for Advancing AI-Driven Metaverse Integration in Smart Healthcare
26. Shaping the Future of Personalized and Predictive Healthcare using AI-Driven Metaverse
- Edition: 1
- Latest edition
- Published: June 1, 2026
- Language: English
JA
Joseph Bamidele Awotunde
Dr. Joseph Bamidele Awotunde is a Lecturer in the Department of Computer Science, Faculty of Communication and Information Sciences, University of Ilorin, Ilorin, Nigeria. His area of research interest cut across Artificial Intelligence, Internet of Things, Cybersecurity, Information Security, Social Computing, Bioinformatics, and Biometrics. He has to his credit over one hundred and fifty publications in reputable outlets like Elsevier, Springer, Hindawi among others covering journals, edited conference proceedings and chapters in books. He was part of the team that won the Artificial Intelligence for Females in Science Technology, Engineering and Mathematics (AI4FS) Grant sponsored by Royal Academy of Engineering (Higher Education Partnerships in sub-Saharan Africa (HEP SSA) 22/24). He is an Associate Editor of the PLOS ONE, and IEEE Transactions on Neural Systems & Rehabilitation Engineering. He is also a member of many professional bodies within and outside Nigeria such as International Association of Engineers and Computer Scientist (MIAENG), Computer Professional Registration Council of Nigeria (MCPN), and Nigeria Computer Society (MNCS). Internet Society.
Affiliations and expertise
Lecturer, University of Ilorin, Ilorin, NigeriaFS
Fuqian Shi
Fuqian Shi (Senior Member, IEEE) received the Ph.D. degree in engineering from the College of Computer Science and Technology, Zhejiang University. He was a Visiting Associate Professor with the Department of Industrial Engineering and Management System, University of Central Florida, Orlando, FL, USA, from 2012 to 2014. He is currently an Associate Professor with the Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA. He serves more than 30 committee board memberships for international conferences. He has published more than 80 journal articles and conference proceedings. His research interests include fuzzy inference system, artificial neuro networks, and biomechanical engineering. He also serves as an Associate Editor for the International Journal of Ambient Computing and Intelligence (IJACI), the International Journal of Rough Sets and Data Analysis (IJRSDA), and a Special Issue Editor of fuzzy engineering and intelligent transportation in Information: An International Interdisciplinary Journal
Affiliations and expertise
Rutgers Cancer Institute of New Jersey, USASK
Subhendu Kumar Pani
Subhendu Kumar Pani received his Ph.D. from Utkal University Odisha, India. He has more than 16 years of teaching and research experience. His research interests include data mining, big data analysis, web data analytics, fuzzy decision making and computational intelligence. He is a fellow in SSARSC and life member in IE, ISTE, ISCA, OBA.OMS, SMIACSIT, SMUACEE, CSI.
Affiliations and expertise
Department of Computer Science and Engineering, Krupajal Engineering College, Bhubaneswar, Odisha, IndiaJS
Jing Shi
Dr. Jing Shi is a full professor of Mechanical Engineering, as well as the director for the Industrial & Systems Engineering program at University of Cincinnati, USA. His expertise covers renewable energy, materials and manufacturing for energy conservation, green manufacturing, energy economics, modeling and optimization of complex systems, Industry 4.0, digital manufacturing, manufacturing innovations, big data analytics, and circular economy. He has published more than 200 refereed papers in technical journals and conference proceedings, and he has been recognized as a top 2% most widely cited scientist, in the list of “the World's Top 2% Scientists”, published by Stanford University. He currently serves as an Area Editor for Computers and Industrial Engineering, an Associate Editor for International Journal of Green Energy, as well as an editorial member of multiple international journals. He is also a frequent recipient of teaching and research awards, a regular reviewer for federal funding agencies, and a conference organizer of numerous international conferences.
Affiliations and expertise
University of Cincinnati, OH, USA