Smart sensing for cholesterol quantification: integrating AI, IoT, and emerging technologies in coronary artery disease risk management

Coronary artery disease (CAD), primarily caused by high cholesterol levels, continues to be a major contributor to death across the globe. Despite the availability of traditional laboratory-based cholesterol detection methods, these approaches are often limited by high costs, time consumption, the need for skilled personnel, and incompatibility with point-of-care (POC) applications, underscoring the urgent need for innovative, accessible, and rapid diagnostic tools. This review presents a comprehensive analysis of emerging smart bio-sensing technologies tailored for all lipid biomarkers, including cholesterol detection, focusing on integrating artificial intelligence (AI), Internet of Things (IoT), and advanced biosensing platforms. It explores the latest developments in electrochemical, optical, microfluidic, and wearable biosensors, evaluating their performance in sensitivity, specificity, miniaturization, and real-time data acquisition. Emphasis is placed on the role of nanomaterials, lab-on-chip systems, aptamer-based sensing, and field-effect transistor (FET) architectures in enhancing detection accuracy and portability. A unique feature of this review is the material-centric classification of biosensors, linking substrate choices to cost, flexibility, and POC suitability. Furthermore, integrating AI/ML algorithms and IoT connectivity for data processing, remote monitoring, and predictive analytics is highlighted as a transformative trend in next-generation diagnostics. The review also addresses commercialization pathways, regulatory considerations, and user-centric design principles for translating lab innovations into scalable, accessible solutions. By bridging biosensing innovations with digital technologies, this review outlines a strategic roadmap for deploying smart, connected, and personalized Cholesterol monitoring systems for effective CAD risk management. • The review presents a comprehensive overview of conventional and emerging techniques for cholesterol detection, emphasizing their role in cardiovascular disease diagnostics. • Identifies key barriers such as regulatory approval, scalability, cost-efficiency, and integration with electronic health systems in bringing biosensors from the lab to the bedside. • Discusses the integration of AI/ML algorithms for real-time data interpretation, trend prediction, and personalized risk profiling in next-generation cholesterol monitoring platforms. • This review outlines current trends and future directions in integrating biosensors with digital health technologies for early CAD risk assessment and personalized patient care.