| Title | AI-Based Remote Patient Monitoring Systems for Chronic Disease Management |
| Research Area | Artificial Intelligence |
| Abstract | Chronic diseases such as diabetes, hypertension, and cardiovascular disorders require continuous monitoring to prevent medical emergencies and improve quality of life. Traditional healthcare systems rely on periodic clinical visits, resulting in delayed diagnosis and inadequate disease management. Artificial Intelligence (AI)-based Remote Patient Monitoring (RPM) systems provide continuous health tracking using wearable sensors, mobile health platforms, and machine learning algorithms. This paper presents an AI-driven RPM model capable of predicting patient deterioration, identifying abnormal patterns, and assisting physicians with timely interventions. Experimental evaluations indicate that AI-enhanced RPM reduces emergency hospital admissions by 25–30% and improves treatment adherence among long-term patients. The study further discusses challenges related to data privacy, interoperability, and real-time analytics, offering future directions for scalable and secure RPM deployment. |
| Keywords | Remote Patient Monitoring, Artificial Intelligence, Health Analytics, Chronic Disease Management, IoT Sensors, Predictive Healthcare |
| Paper Status | Published |
| Volume | 1 |
| Issue | 5 |
| Published On | 17/10/2025 |
| Published File |
IJSRTD_4861.pdf
|