Digital Medical Twins for Personalized Healthcare: Generative AI and Multi-Modal Approaches for Predictive Diagnostics and Precision Medicine

· · ·
· Academic Press
Ebook
370
Pages
Eligible
This book will become available on February 1, 2027. You will not be charged until it is released.

About this ebook

Digital Medical Twins for Personalized Healthcare: Generative AI and Multi-Modal Approaches for Predictive Diagnostics and Precision Medicine presents a rigorous, evidence-based framework for building patient-specific digital replicas that support clinical decision-making and research innovation. The field integrates electronic health records, imaging, genomics, and continuous wearable data into dynamic models that simulate disease trajectories and treatment responses. Readers confront a evolving landscape of AI methods, privacy requirements, and regulatory expectations; this book offers a coherent, practical reference designed for scientists, and health-system professionals seeking to translate digital twins from concept to care delivery. - Provides foundational concepts and practical guidance for digital medical twins - Integrates generative and multi-modal AI to enhance patient data modeling - Addresses explainability, privacy, and regulatory alignment for clinical data use - Offers cross-disciplinary case studies across cardiology, oncology, neurology, and preventive care
Digital Medical Twins for Personalized Healthcare: Generative AI and Multi-Modal Approaches for Predictive Diagnostics and Precision Medicine presents a rigorous, evidence-based framework for building patient-specific digital replicas that support clinical decision-making and research innovation....

About the author

Dr. Yogesh Kumar is an Assistant Professor in the CSE Department at PDEU, holding a Ph.D., M.Tech, and B.Tech in Computer Science and Engineering. He is a distinguished expert in Artificial Intelligence applications in healthcare with over a decade of experience. His work spans deep learning–based disease detection, predictive modeling for chronic illnesses, medical imaging analytics, and ethical AI deployment—recognised among the top 2% of global scientists by Stanford University in both 2023 and 2024. He has authored more than 200 publications (including over 100 Web of Science–indexed), led national consultancy and funded research projects, and played key roles in editorial and peer-review capacities for top-tier journals and conferences

Dr. Apeksha Koul is an Assistant Professor in School of Computer Science Engineering and Technology, Bennett University. She earned her B.E. in Computer Science & Engineering (VPKBIET, Pune), M.Tech (Shri Mata Vaishno Devi University, Katra), and Ph.D. (Punjabi University, Patiala). Dr. Koul specializes in Artificial Intelligence and Machine Learning, with a primary research focus on medical image analysis and diagnostics. In collaboration with leading researchers, her recent contributions feature the development of advanced Convolutional Neural Network (CNN) models optimized for deep learning-based airway disease detection. Dr. Koul is known for her adaptive leadership in research and teaching, and her ability to optimize interdisciplinary projects with effective communication and resource management.

Dr. Nandini Modi is an Assistant Professor at PDEU with a Ph.D. focused on eye-gaze tracking in human–computer interaction. She is an IEEE member and her research interests encompass computer vision, cognitive computing, sentiment analysis, and smart healthcare solutions. She has contributed to various international conferences and journals and holds intellectual property rights for several innovations. Dr. Modi also serves as a reviewer for reputed journals and is active in professional communities.

Dr. Shakti Mishra is an Associate Professor and Head of CSE at PDEU. His academic credentials include a Ph.D. and B.Tech, and his expertise spans distributed computing, cloud computing, energy-efficient systems, and machine learning for renewable energy. His publication record includes works on cloud ontology, load balancing, fraud detection, fake-news detection, solar prediction, and smart systems.

Dr. Yogesh Kumar is an Assistant Professor in the CSE Department at PDEU, holding a Ph.D., M.Tech, and B.Tech in Computer Science and Engineering. He is a distinguished expert in Artificial Intelligence applications in healthcare with over a decade of experience. His work spans deep learning–base...

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