Medical Robotics and Computer Assisted Surgery: AI-enhanced, Data-driven, and Evidence-based Approaches – serie

Medical Robotics and Intelligent Healthcare Technologies: AI Applications to Improve Healthcare in Developing Countries is an essential reference for professionals and researchers in fields such as biomedical engineering, health informatics, bioelectronics, medical electronics, physiology, and robotics. This book presents the latest advancements in AI and machine learning-based robotics applications, enabling readers to quickly grasp effective methods and compare various approaches. Addressing critical research areas that directly impact human life and health fills a significant market gap, providing valuable insights into AI, deep learning, machine learning, and big data analytics methods as they relate to emerging robotics research models in the biomedical and healthcare domains.It offers a comprehensive understanding of the concepts, themes, and tools in medical robotics, IoT-based applications, edge computing, and artificial intelligence. With a specific focus on developing countries, this book catalyzes healthcare progress by showcasing the potential of medical robotics and intelligent healthcare technologies. It aims to empower researchers and practitioners to harness these state-of-the-art technologies to improve healthcare systems worldwide.

Provides comprehensive coverage of the concepts, themes, and tools in the field of medical robotics, IoT-based applications, edge computing, and artificial intelligenceEmphasizes the automation of robotic tasks to achieve a low-cost and secure operational environmentFocuses on the application of robotics in medical diagnosis and various medical fields, including rehabilitation, surgery, different diagnostic stages, and medical and surgical training

AI-Powered Developments in Medical Robotics: Data-Driven Techniques for Enhanced Surgical Efficiency offers a comprehensive exploration of AI-driven innovations, robotics, and data-driven techniques specifically tailored for medical applications. The book strikes a balance by addressing foundational principles, emerging technologies, and their practical implementation in real-world scenarios. It enhances its value through the inclusion of real-world case studies and interdisciplinary perspectives, making it relevant for professionals, researchers, and students alike. The book explores future developments, such as augmented and virtual reality in medical robotics, positioning itself as a forward-thinking resource.By addressing current gaps in the field, including regulatory challenges, training needs, and cost-effectiveness, the book ensures a well-rounded approach that appeals to both advanced and emerging markets. This multifaceted perspective enriches the reader's understanding and equips them with actionable insights for navigating the complexities of AI-driven healthcare robotics. The book serves as a definitive reference for a global audience seeking innovation and practical solutions in the rapidly evolving landscape of medical technology, bridging the gap between theory and practice in a critical area of healthcare advancement.Offers a thorough integration of AI-driven innovations, robotics, and data-driven techniques specifically tailored for medical applicationsAddresses foundational principles, emerging technologies, and practical implementation, providing a well-rounded perspective that combines theory with real-world applicationIncludes real-world case studies and insights from various disciplinesDiscusses future advancements, such as augmented and virtual reality in medical robotics

Deep Learning for Cardiac Signal Analysis in Robotic Applications delves into the transformative role of artificial intelligence in enhancing robotic-assisted cardiovascular procedures. The book starts with the fundamentals of cardiac signals and deep learning, introducing key heart modalities, including the physiological underpinnings and challenges of signals like ECG and BCG and an overview of deep learning architectures relevant to signal processing. Pre-processing and feature extraction techniques are detailed to prepare readers for advanced analysis. Other sections focus on AI-enhanced cardiac signal analysis, covering arrhythmia detection, myocardial ischemia diagnostics, hypertension monitoring via BCG, and explainable AI approaches for fetal arrhythmia monitoring.The final section integrates AI with robotic cardiac surgery, addressing real-time signal integration, AI-guided intervention precision, intraoperative decision support, postoperative monitoring, and future trends in cardiac AI and robotic-assisted surgery. Addressing the complexities of heart signal interpretation amidst the dynamic environment of cardiac surgery, this book meets the critical need for a comprehensive resource that bridges deep learning advances with practical surgical applications. It responds to the challenge of understanding intricate bio-signals, such as ECG, VCG, and BCG, by providing clear explanations, case studies, and methodological insights tailored to improve surgical precision, safety, and patient outcomes.Bridges deep learning techniques with practical cardiac robotic surgery applications for improved clinical outcomes and safetyOffers clear explanations and case studies to simplify complex AI concepts for multidisciplinary audiencesProvides comprehensive coverage of cardiac signal processing, including noise reduction and anomaly detection methodsHighlights real-time AI integration for enhanced surgical decision-making and precision in robotic interventionsExplores ethical, regulatory, and future trends in AI-assisted cardiac healthcare and robotic surgery advancements

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