Comprehensive review on IOT-based smart health risk prediction system using AI and biomedical sensors

Abstract

This paper presents a comprehensive review of IoT-based smart health risk prediction systems that integrate Artificial Intelligence (AI), biomedical sensors, and Internet of Medical Things (IoMT) technologies for advanced healthcare monitoring and chronic disease management. The study discusses the architecture of IoMT systems, which utilize wearable and implantable sensors to continuously collect physiological and biomedical data from patients in real time. These data are analyzed using Machine Learning (ML) and Deep Learning (DL) algorithms for disease prediction, classification, and early diagnosis of conditions such as cardiovascular diseases, diabetes, and respiratory disorders. The review highlights the major components of IoMT systems, including data acquisition, wireless data transmission, intelligent data processing, and healthcare user interfaces for clinical decision-making. Communication technologies such as Bluetooth, Wi-Fi, and edge computing are discussed for enabling efficient and low-latency healthcare monitoring. The paper also examines interoperability standards including HL7 and FHIR for secure and scalable healthcare data exchange. Furthermore, major challenges associated with IoMT healthcare systems, such as data privacy, cybersecurity, interoperability, scalability, and AI bias, are critically analyzed. Emerging technologies including Federated Learning, blockchain, Explainable AI (XAI), and energy-efficient wearable sensors are also reviewed as future research directions for intelligent healthcare systems. The review demonstrates that AI-integrated IoMT systems can importantly improve remote patient monitoring, predictive healthcare analytics, and personalized medical services.