Scope of the Book
The scope of this proposed edited book is to discuss the theoretical foundations, the emergence, and the application areas of artificial intelligence in the healthcare domain in an easy-to-understand manner that would be highly helpful for researchers, academicians, and industry professionals. This edited book will also set the trend to evolve new concepts and ideas and share issues and challenges faced during the research with probable solutions applied to AI for healthcare which may be integrated with other computing techniques such as Medical IoT and Blockchain. This proposed book presents the evolution of AI in healthcare, from fundamental theories to present forms, and explains the concepts of artificial intelligence in healthcare, specifically, AI-assisted drug discovery, NLP for intelligent healthcare, AI for precision medicine, AI for virtual nursing, Epidemiology, and also covers a variety of applications with real-world case studies in these areas. The recent developments, such as large biomedical language models and One Health initiatives, will also be discussed.

Table of Contents (Tentative)

Robotics in Healthcare: Robotic Nurses, Ambulance Drones, Surgery Robots, Tele-assistance robots and virtual agents, Cleaning or disinfection robots, Machine vision applications for robotics in healthcare, Future of biomedical robots, Challenges and trends in rehabilitation devices in healthcare, Robotic interactions and control for wearable devices, Bio-inspired medical robots, Safety in close human-robot interaction, Bio-sensors.

Medical Internet of Things (MIoT): IoT-enabled Biosensors, IoT-based disease monitoring systems, Internet of nano-things for healthcare applications, Wearable MIoT devices for accessible healthcare services, Degradable MIoT sensors for healthcare applications, MioT in medical implant manufacturing, MIoT in genomics, MIoT and pharmaceutical industry applications, MIoT challenges – Data security, privacy, and ethical considerations.

Perception in Healthcare: Image recognition, Magnetic Resonance Imaging applications in Healthcare, X-Ray, CT Imaging, AI in bone imaging, AI in brain imaging, AI in endoscopy, Molecular imaging using AI, AI for Radionuclide imaging, medical image segmentation, AI for neuro-imaging applications, Deep Learning for Segmentationof Brain Images, Early Detection of Alzheimer’s Disease using AI techniques.

Natural Language Processing in Healthcare: Cognitive Virtual Agents, Information extraction from HER, Classification of EHR, Clinical Documentation, Speech recognition in healthcare and applications, Computer-assisted coding, Automated registry reporting, AI for clinical decision support, AI for clinical trial matching, Drug-drug interaction extraction, Drug-protein interaction extraction, medical relation extraction, AI for computational phenotyping, AI Chatbots and Virtual Scribes, Dictation and EMR Implications, Root Cause Analysis in healthcare using NLP techniques.

Multi-Agent Systems in Healthcare: AI for clinical decision-making, Collaborative agents in healthcare, Care teams management, Agent-based methods for medical image segmentation or classification, Agent-based decision support systems for disease diagnosis, Agent-based medical data processing, HIS interoperability and integration, Mobile agents in hospital environments, Patient empowerment and chronic disease self-management through personalized agent-based systems and models, Clinical agent-based decision support systems, Personalized recommender systems in healthcare, Conversational agents and personal digital assistants for patient empowerment.

Communities in Healthcare: AI applications for Information sharing in Healthcare, Infodemiology, Social Media and Health, combating misinformation, raising public awareness, AI for promoting citizen engagement in healthcare, computing-oriented healthcare informatics, Analytics, Systems and Human Factors for Healthcare, Computational social systems for healthcare, medical data mining, knowledge graphs in healthcare, graph-based collaborative intelligence in healthcare, social media for mental health, managing pandemics using healthcare communications.

Generative AI and Healthcare: Generative AI for Clinical Documentation, Clinical document summarization using Generative AI, Biomedical Large Language Models – Pretraining and finetuning, Healthcare data labeling and augmentation using Generative AI, Personalized healthcare using Generative AI, Clinical Question Answering using Generative AI models, Medical Imaging applications using Generative AI, Challenges of Generative AI in Healthcare – Privacy, Discrimination and Bias, Misuse and Over-reliance on Generative AI.

Hybrid AI systems for Healthcare: Artificial Intelligence and Blockchain for Trust-free, Patient-driven healthcare applications, Blockchain, AI and Big Data for Biomedical devices; Blockchain for secure, reliable, and resilient healthcare ecosystems; Extended and Virtual Reality applications in healthcare–surgical procedures, behavioral and mental health, medical training, disability and elder care, Extended Reality-supported clinical trials, Metaverse and Healthcare applications, Challenges – Security, Privacy and ethical considerations for Hybrid AI systems.

Machine Learning for Healthcare: Biomedical signal and image processing Multimodal information fusion for Healthcare, Reinforcement learning in healthcare, Mathematical and statistical models for healthcare, Pattern recognition in biomedical applications, Advanced molecular/pathologic image analysis, Supervised and unsupervised learning in clinical applications, Gait, and motion analysis using complex biomedical signals, Brain-computer interfacing, Challenges of machine learning in healthcare applications

Ethics in Healthcare: Ethical considerations in Healthcare AI, Security and Privacy concerns in Healthcare AI, Ethics in biomedical research, Ethics in clinical practice and clinical trials, Ethics in medical law, public health, and health policy, Bioethics and meta-bioethics, judicial decisions and legislation relating to medical issues, Ethics in omics research and human enhancements, Moral issues in healthcare, Ethical frameworks in healthcare, Ethical considerations in human genome editing

Explainable Healthcare AI: Role of Explainable AI in healthcare, Interpretable healthcare AI models, AGI for explainable AI in Healthcare, Explainable healthcare frameworks, Human-AI relationships for explainable AI, Explainable AI in medical imaging, Interpretability of healthcare models, Healthcare decision support systems and explainable AI, Federated Learning and Explainable AI

Accessible and Sustainable Healthcare: Health Equity, One Health initiatives, Accessible healthcare, Health disparities, Poverty and health, Social Injustice and Public Health, Racial inequalities in healthcare, Rural health equity, Frameworks for sustainable healthcare, risks of global environmental crisis in healthcare, Contributions of health sector to the global environmental crisis, Alternate energy generation for healthcare infrastructure, Energy efficient healthcare, Healthcare organization sustainability strategies, Sustainable healthcare challenges.