Evolution of Computing Paradigms in Healthcare: From Sensor Networks to Agentic AI Systems

Over the past two decades, healthcare has experienced a steady transformation driven by advances in computing and information and communication technologies. Early efforts focused on digitizing clinical data and enabling basic networked systems within healthcare institutions. Subsequent developments introduced wireless sensor networks and body area networks, enabling continuous and patient-centric data acquisition. The emergence of the Internet of Things further expanded connectivity and scalability, supporting remote monitoring and large-scale data integration. More recently, advances in artificial intelligence have transformed connected healthcare systems into intelligent platforms capable of prediction and automated analysis. The latest evolution, characterized by large language models and agentic AI systems, introduces reasoning, contextual understanding, and autonomous coordination into healthcare workflows. This paper presents a unified evolutionary analysis of computing paradigms in healthcare, tracing their progression from early digital systems to contemporary agentic AI frameworks. By adopting a layered, system-level perspective encompassing sensing, communication, computation, intelligence, and autonomy, the paper clarifies the relationships among diverse healthcare technologies. The proposed framework provides a coherent conceptual foundation for understanding current systems and guiding future research in intelligent and autonomous healthcare.