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T. Selvi is an Assistant Professor in the Department of Computer Applications at Government Arts College (Autonomous), Kumbakonam, Tamil Nadu, India. Her academic responsibilities include teaching and research in computer applications. Her professional role supports computing education and technology-related learning. Her contribution to this chapter focuses on IoT-enabled remote health monitoring and AI-based disease risk prediction.
The amalgamation of the Internet of Things (IoT) with Artificial Intelligence (AI) is revolutionising telemedicine through the facilitation of ongoing physiological surveillance, astute health data evaluation, and anticipatory illness risk assessment. Conventional healthcare frameworks frequently depend on intermittent clinical evaluations and hospital-centric assessments, which could fail to reflect alterations in a patient's health status between appointments. Wearable devices equipped with IoT technology, biomedical sensors, and interconnected medical apparatus can gather physiological data such as heart rate, blood pressure, oxygen saturation, body temperature, glucose levels, and physical activity. Artificial intelligence methodologies can scrutinise this data to detect irregular patterns, evaluate any health hazards, and facilitate prompt clinical evaluation. The section additionally addresses data preprocessing, model creation, performance assessment, privacy safeguarding, cybersecurity, interoperability, and clinical verification. Innovative methodologies including edge AI, federated learning, digital health twins, and tailored risk modelling are regarded as prospective avenues for research. The section outlines a theoretical structure for creating scalable and accountable remote healthcare systems, highlighting the necessity for AI-generated risk assessments to be verified and interpreted by competent healthcare practitioners.