This book offers a comprehensive and technically rigorous exploration of the rapidly evolving field of Artificial Intelligence–enabled Internet of Medical Things (AIoMT). It presents a multidimensional examination of how AI methodologies and interconnected medical devices are transforming modern healthcare ecosystems into intelligent, autonomous, and data-driven systems.
The volume covers the complete lifecycle of AIoMT systems, beginning with their conceptual evolution and foundational architectures, followed by the electronic components, communication protocols, and network infrastructures that power intelligent medical devices. It delves deeply into the integration of machine learning, deep learning, and reinforcement learning into IoMT frameworks, enabling predictive diagnostics, autonomous decision-making, and personalized clinical interventions.
A major emphasis is placed on cybersecurity, providing in-depth analyses of system vulnerabilities, threat vectors, adversarial attacks, and robust cryptographic techniques. The book proposes actionable security and resilience strategies to ensure data integrity, patient privacy, and dependable system performance in real-world scenarios.
In addition to theoretical foundations, the volume presents empirical case studies demonstrating AIoMT deployment in areas such as medical imaging, robotic-assisted surgery, telemedicine, smart wearables, and remote patient monitoring. It further discusses benchmarking methodologies, validation techniques, and real-world implementation challenges.
Concluding with future directions, ethical considerations, and standardization needs, this book serves as an essential resource for AI researchers, biomedical engineers, IoT developers, healthcare technologists, clinical innovators, and policymakers engaged in shaping next-generation intelligent medical infrastructures
