International Journal of Science, Engineering and TechnologyAuthors: Thorat Pradnya Ajit, Prof. S. S. Bhor, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: —Continuous monitoring of patient health is essential for early diagnosis and timely treatment, but conventional care depends on periodic manual checks that can miss critical changes between rounds. This paper reviews AI-based smart health monitoring for hospitals and home care. It examines IoT-enabled wearable sensors and medical devices that measure heart rate, temperature, blood oxygen saturation, blood pressure and activity; clinical early warning scores that convert vital signs into a risk score; and machine learning models that predict clinical deterioration more accurately than conventional regression, along with deep learning on electrocardiograms for arrhythmia detection, continuous prediction of acute kidney injury from electronic health records and large-scale smartwatch studies of atrial fibrillation. Public critical-care databases that enable model development are discussed. CNNs, random forests and SVMs for anomaly detection and classification of patient status as normal, warning or critical are compared using accuracy, precision and recall. A five-layer architecture comprising sensing, edge processing, secure communication, clinical analytics and care-team dashboard layers is proposed, together with research gaps, objectives, a comparison of monitoring approaches and challenges such as alarm fatigue, data quality, privacy, interoperability and clinical validation. The review concludes that AI assisted monitoring can enable earlier intervention when designed to support clinicians.
DOI: https://doi.org/10.5281/zenodo.24217444
