Step into the next dimension of healthcare with this essential guide to the Medical Metaverse, where AR, VR, and AI converge to redefine patient engagement, medical training, and surgical precision.
The rapid evolution of digital technologies is transforming every aspect of human life, and healthcare is no exception. Originally rooted in entertainment and social interaction, the concept of the metaverse is now permeating the medical field, giving rise to what we call the ‘Medical Metaverse’. The metaverse, a shared virtual space combining physical and digital realities, is poised to offer novel ways to deliver patient care, improve medical training, and enhance patient engagement. This book delves into the exciting and rapidly evolving intersection of healthcare and metaverse technologies. It explores how augmented reality (AR), virtual reality (VR), artificial intelligence (AI), and other emerging digital tools are revolutionizing medical practices. This book is an essential guide for understanding the technologies that are transforming the healthcare landscape and how they can be applied in practical scenarios.
In addition to providing a comprehensive overview of these technologies, the book dives into real-world applications. From telemedicine and remote surgeries using AI-driven diagnostics and personalized treatments, the book highlights key innovations that are already making an impact. The authors discuss case studies, practical examples, and the potential for these technologies to address global healthcare challenges, such as improving access to care, reducing costs, and enhancing patient outcomes. Overall, this book provides a holistic understanding of both the promises and the pitfalls of the medical metaverse, making it a valuable resource for healthcare professionals, technologists, policymakers, and researchers alike.
Readers will find the volume explores advanced applications of augmented reality (AR), virtual reality (VR), artificial intelligence, and deep and machine learning algorithms for predictive modeling; showcases cutting-edge innovations emerging from research laboratories with the potential to transform factory-floor operations; demonstrates key risk factors, success factors, and performance metrics that can inform future research and development; and introduces advanced techniques for efficiently addressing the challenges of implementing real-time metaverse applications in healthcare.
R. Nidhya, PhD is an Assistant Professor in the Department of Computer Science and Engineering, Madanapalle Institute of Technology and Science (Deemed to be University), Andhra Pradesh, India. She has published many research papers in international journals and conferences. Her research interests include wireless body area networks, network security, and data mining.
D. Pavithra, PhD is an Associate Professor at the Dr. NGP Institute of Technology, Coimbatore, Tamil Nadu, India. She completed her PhD at Anna University Chennai in 2021. Her current research interests include autism, machine learning, and deep learning.
S. Balamurugan, PhD is the Director of Albert Einstein Engineering and Research Labs and the Vice Chairman of the Renewable Energy Society of India. He also serves as a research consultant to many companies, startups, SMEs, and MSMEs. He has published 57 books, more than 300 international journals and conferences, and 100 patents.
A. Dinesh Kumar is an Associate Professor in the Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation University, Guntur, Andhra Pradesh, India. He completed his PhD at Anna University Chennai in 2018. His current research interests include wireless body area networks, wireless sensor networks, network security, and artificial intelligence.
Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design at the National Taipei University of Business, Taiwan. He has edited several special issues of journals and published more than 100 research papers. His research interests include designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and networks.
