Rupali Atul Mahajan – författare

Digital Twins and Predictive Modeling in Robotic Surgery addresses the transformative convergence of digital twin technology, predictive analytics, and robotic surgery. This reference responds to the critical need for a comprehensive resource that bridges simulation science, artificial intelligence, and real-time robotic execution, facilitating enhanced surgical precision, patient-specific planning, and intraoperative decision-making. The content is organized into four parts covering foundational intelligent surgical systems, predictive modeling and simulation, clinical applications with case studies, and challenges alongside future directions. Topics include AI integration, IoT-enabled sensor networks, digital twin architectures encompassing the entire surgical ecosystem, surgical workflow modeling, risk assessment, human-robot interaction, technology acceptance, ethical and regulatory considerations. Real-world examples from smart hospitals worldwide illustrate practical implementation. This books benefits biomedical engineers, researchers, and healthcare system architects by providing interdisciplinary insights, practical implementation frameworks, and cutting-edge case studies. It supports improved surgical planning, precision, and decision-making, empowering end users to advance the adoption of intelligent, AI-powered robotic surgery systems in clinical practice and research environments.Bridges clinical, engineering, and data science domains with practical workflows and system architecturesOffers structured frameworks for virtual patient and operating room digital twin modeling and predictive analyticsIncludes expanded real-world case studies and practical implementation strategies from smart operating rooms globallyPresents dedicated chapters on technology acceptance, human-computer interaction, and ethical AI in robotic surgeryProvides insights on regulatory, validation, and clinical trial considerations for AI-enabled surgical systems

Artificial Intelligence (AI) is reshaping modern healthcare in remarkable ways. This book explores how multimodal generative AI integrates clinical, genetic, and imaging data to improve the understanding and management of neurodegenerative diseases. Bridging technology and medicine, it offers accessible insights into next-generation precision healthcare and its real-world impact. Designed for researchers, clinicians, and informed readers alike, it highlights practical applications, ethical considerations, and future possibilities in AI-driven neurological care.This volume provides a comprehensive examination of how multimodal generative AI techniques can be applied to the diagnosis, monitoring, and management of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. It covers foundational concepts in precision healthcare, multimodal data integration (including genetic, clinical, and neuroimaging data), and advanced modeling approaches for disease progression prediction. The book also explores synthetic data generation for rare conditions, explainable and interpretable AI models for clinical decision support, and AI-driven early detection systems. Dedicated chapters address ethical, legal, and regulatory considerations, interdisciplinary collaboration, and patient-centric AI design. Through case studies and real-world applications, the book demonstrates how intelligent systems can enhance personalized treatment planning, improve clinical outcomes, and support data-driven healthcare innovation.