Amidst the growing trend of chronic disease and the need for continuous, longitudinal care focused on the patient, Remote Patient Monitoring (RPM) systems have been on the rise. This research aims to assess the effectiveness of Artificial Intelligence (AI) and the Internet of Things (IoT) in addressing the efficiency, sensitivity, and generalizability of RPM systems. This research is qualitative and quantitative in nature, utilizing biological real-time signals from publicly available datasets (MIT-BIH, MIMIC-III, Fitbit), employing AI methodologies (Random Forest and Convolutional Neural Network (CNN)) for classifying and predicting anomalies. The proposed edge-enabled Internet of Things architecture lowers latency by 35%; CNNs achieve 93.2% accuracy in electrocardiograms (ECG) classification. Qualitative subject-matter expert responsiveness from healthcare professionals noted a 40% increase in timely intervention for detected anomalies—with confidence in the usability of the systems. Findings advocate AI and IoT enhancements for smart real-time monitoring of health-related information.
Keywords: Convolutional Neural Networks (CNN), Edge Computing, Healthcare Informatics, Internet of Things (IoT), IoMT, Physiological Signal Analysis, Remote Patient Monitoring (RPM), Smart Wearables, artificial intelligence (AI), predictive analytics
