Next-Gen Remote Patient Monitoring: An AI–IoT–Edge Convergence for Proactive Healthcare

Abstract

Remote Patient Monitoring (RPM) is a groundbreaking means of delivering continuous healthcare, but extant solutions endure shortfalls of high latency, lack of scalability, cloud connectivity reliance, and absence of privacy. The current paper reveals a next-generation architecture of RPM that aligns Artificial Intelligence (AI), Internet of Things (IoT), and Edge Computing into a single, end-to-end ecosystem facilitating proactive healthcare. Wearable and environmental biosensors at the sensing layer capture vital signs of heart rate, SpO2, temperature, blood pressure, and electrocardiogram waves. A congestion-aware IoT networking layer guarantees transfer of data with reliability across community-scale deployments, and lightweight preprocessing, anomaly identification, and emergency alerting occur at edge gateways. Hybrid intelligence results by virtue of edge–cloud collaboration, whereby TinyML models execute immediate-time anomaly identification and cloud-based machine learning (e.g., XGBoost, deep temporal models) execute predictive risk stratification. Endowing regulation and ethical compliance, privacy-preserving learning strategies (federated model updating and differential privacy) are adopted. The system is measured relative to threshold-only, edge-only, and cloud-only baselines and seems to achieve improvements relative to detection accuracy, latency, and energy efficiency; and a clinical pilot validates decreased readmissions and expanded patient safety. By virtue of bundling of technical novelty and clinical evidence, this research discloses AI–IoT–Edge convergence to represent a scalable direction to smart, proactive, and privacy-preserving RPM systems.