AI-enabled remote patient monitoring systems enhancing chronic disease management, reducing hospital readmissions and improving healthcare accessibility worldwide

The rapid rise in chronic disease prevalence has exposed critical limitations in episodic, facility-centered healthcare systems, particularly in managing long-term conditions such as cardiovascular disease, diabetes, and chronic respiratory disorders. AI-enabled remote patient monitoring (RPM) systems offer a paradigm shift by embedding continuous, real-time clinical surveillance into patients’ daily lives. These systems integrate wearable biosensors, mobile health applications, and cloud-based machine learning models to generate actionable insights from high-frequency physiological data streams. This study examines how AI-driven RPM enhances chronic disease management by enabling early detection of clinical deterioration through predictive risk scoring, anomaly detection, and personalized baseline modelling. By identifying deviations in vital parameters such as heart rate variability, glucose trends, and oxygen saturation AI systems support timely clinical intervention, thereby reducing avoidable hospital readmissions. Furthermore, the architecture of AI-enabled RPM facilitates decentralized care delivery, improving access for geographically isolated and resource-constrained populations while reducing dependency on hospital infrastructure. The analysis also highlights the role of adaptive algorithms in tailoring treatment pathways, improving medication adherence, and supporting clinician decision-making through automated alerts and prioritization systems. While demonstrating measurable gains in care continuity and system efficiency, the study critically evaluates challenges related to data governance, algorithmic bias, and interoperability within heterogeneous healthcare ecosystems.