AI & ML Based Health Care Devices & Smart Applications

Healthcare stands at the threshold of a new era—one defined not only by medical discovery, but by intelligent systems capable of learning, adapting, and transforming care delivery at scale. Artificial Intelligence (AI) and Machine Learning (ML) are rapidly reshaping global health ecosystems, influencing everything from early disease detection and precision therapeutics to public health surveillance, hospital automation, and equitable access to care. What was once conceptual is now operational; what was once experimental is now mission-critical.

AI & ML Based Health Care Devices & Smart Applications emerges at this pivotal moment. This volume brings together interdisciplinary scholarship that reflects the convergence of computational intelligence, biomedical innovation, data science, engineering design, and health policy. The chapters collectively examine not only how intelligent systems are built, but how they are deployed, governed, validated, and scaled across diverse healthcare environments—ranging from technologically advanced hospitals to resource-constrained settings.

The transformative promise of AI in healthcare extends beyond efficiency and automation. Intelligent diagnostic platforms can expand access to expert-level interpretation in underserved regions. Predictive analytics can strengthen preventive care and population health management. Smart medical devices and Internet of Medical Things (IoMT) infrastructures can enable continuous, remote, and personalized monitoring. Natural Language Processing (NLP) can unlock insights from vast clinical records, while robotics and intelligent automation are redefining surgical precision and rehabilitative care. Together, these technologies are laying the foundation for a proactive, data-driven, and patient-centred healthcare paradigm.

However, technological capability alone does not guarantee societal benefit. As AI systems become embedded in clinical workflows and national health infrastructures, questions of ethics, transparency, accountability, data governance, regulatory oversight, and algorithmic fairness become central. Global disparities in digital infrastructure, data access, and policy readiness must be addressed to prevent widening health inequities. Responsible innovation requires collaborative engagement among researchers, clinicians, policymakers, industry leaders, and civil society.

This book, therefore, places equal emphasis on technical rigor and systemic responsibility. Alongside core machine learning methodologies, architectures, and implementation strategies, the chapters explore validation frameworks, regulatory landscapes, cybersecurity concerns, interoperability standards, and emerging global policy dialogues. The goal is not merely to present technological advancement, but to situate it within the broader context of sustainable, ethical, and inclusive healthcare transformation.

Designed for researchers, students, healthcare professionals, innovators, and policy architects, this volume aims to serve as both a scholarly reference and a strategic guide. Each contribution reflects the understanding that the future of healthcare will be shaped not only by algorithms, but by the frameworks that govern their development and deployment.

As nations strive to achieve universal health coverage, manage aging populations, respond to global health crises, and contain rising costs, AI and ML will play a defining role in shaping resilient and intelligent health systems. The decisions made today—technical, regulatory, and ethical—will influence healthcare outcomes for decades to come. We hope that this book will inspire forward-thinking research, evidence-based policymaking, and globally responsible innovation that advances equitable, patient-centred, and sustainable healthcare worldwide.