AI-DRIVEN MOBILE APPLICATIONS FOR REAL-TIME REMOTE PATIENT MONITORING AND HEALTH MANAGEMENT

Abstract

Remote Patient Monitoring (RPM) systems leveraging AI-based mobile applications are transforming healthcare by enabling real-time, continuous patient monitoring outside traditional clinical settings. These systems integrate wearable sensors, mobile platforms, and cloud-based analytics with artificial intelligence to process vast physiological and behavioral datasets, providing predictive insights, early warning alerts, and personalized recommendations. This thesis examines AI-enhanced RPM, emphasizing computational frameworks, clinical applications, advantages, challenges, and future directions. By automating data interpretation and supporting proactive interventions, AI-driven mobile RPM systems enhance patient outcomes, reduce hospitalizations, and promote patient-centered care.