Artificial Intelligence-Driven Metaverse Integration in Smart Healthcare: Advancing Personalized and Predictive Medical Capabilities

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.

• 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

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.

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

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.

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.