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.

Dr. Manoj Chandra Garg is an Assistant Professor at the Amity Institute of Environmental Sciences, Amity University Uttar Pradesh, with over a decade of teaching and research experience. He earned his Ph.D. from the Indian Institute of Technology (IIT) Roorkee, focusing on solar photovoltaic assisted nanofiltration and reverse osmosis membrane water treatment systems. He also holds an M.Tech. in Environmental Science & Technology from Thapar University, where he was awarded a Gold Medal. Dr. Garg’s research spans water quality, membrane filtration technologies, solar desalination, biosorption, and the application of machine learning and artificial intelligence in environmental systems. He has published extensively with several journal articles and holds various patents. His research has been supported by significant projects, including a grant from the Science and Engineering Research Board (SERB) under the Early Career Research Award. Dr. Garg is actively involved in academic committees and collaborations with international institutions.

Dr. Pinki Sharma completed her M.Tech. in Environmental Engineering from Thapar University and earned her Ph.D. in industrial wastewater treatment using advanced technologies from IIT Roorkee. She has research experience as a Research Associate on the UK-FAR GANGA project at NIH Roorkee and was awarded the DST Women Scientist A Fellowship. Her research interests focus on water and wastewater treatment, membrane technology, electrochemical treatment, capacitive deionization, and other advanced treatment techniques.

Dr. Smriti Agarwal is an Assistant Professor in the Department of Electronics and Communication Engineering at Motilal Nehru National Institute of Technology (MNNIT), Allahabad. She completed her Ph.D. from IIT Roorkee in the area of millimeter wave imaging radar for target detection. Dr. Agarwal’s research interests include microwave and millimeter wave imaging, metamaterials, antenna design, AI and machine learning applications in communication and imaging. She has several published journal articles and has received multiple fellowships and awards including the DST Women Scientist fellowship. She has extensive experience in research and academic leadership roles.

Dr. Monika Simon holds a Ph.D. in Environmental Engineering and Sciences from IIT Roorkee and is currently a Postdoctoral Researcher at SRM University-AP, Andhra Pradesh. She has industrial experience in wastewater treatment and has worked on international collaborative research projects. Dr. Simon’s research focuses on microbial bioremediation, biodegradation of microplastics, and developing sustainable water treatment technologies. She has authored several peer-reviewed articles and holds a patent related to wastewater treatment innovations.

Part I: AI and Machine Learning in Core Wastewater Treatment Processes

Chapter 1: Machine Learning Applications in Wastewater Treatment
Chapter 2: Optimization of Biological Treatment Processes Using AI
Chapter 3: AI for Energy Efficiency in Wastewater Treatment
Chapter 4: AI-Powered Wastewater Treatment System Design and Optimization

Part II: AI-Enabled Monitoring, Control, and Automation

Chapter 5: Artificial Intelligence for Wastewater Quality Monitoring, Process Control, and Automation
Chapter 6: Smart Wastewater Systems: Leveraging AI for Quality Monitoring and Assessment
Chapter 7: AI-Driven Process Control and Automation”

Part III: Decision Support, Evaluation, and Emerging Technologies
 
Chapter 8: AI-Based Decision Support Systems for Wastewater Management
Chapter 9: Future Perspectives and Emerging Trends in AI for Wastewater Treatment
Chapter 10: AI-Enabled Fuzzy Multi-Criteria Approach for Evaluating Wastewater Treatment Technologies
Chapter 11: AI and Internet of Things (IoT) Integration in Smart Wastewater Systems

Part IV: Sustainability and Specialized Applications
 
Chapter 12: AI for Sludge Management and Byproduct Utilization
Chapter 13: Harnessing Artificial Intelligence for Sustainable Management of Saline Wastewater in Rajasthan's Saline Lakes
Chapter 14: Wastewater Reuse Optimization Using Machine Learning