DESIGNING AUTONOMOUS AI AGENTS FOR PERSONALIZED PATIENT SUPPORT AND REAL-TIME HEALTH MONITORING IN INTEGRATED CLOUD ENVIRONMENTS

Abstract

The rapid advancement of artificial intelligence and cloud computing has enabled the development of intelligent healthcare systems that provide continuous and personalized patient support. This paper proposes the design of autonomous AI agents for real-time health monitoring and personalized patient assistance within integrated cloud environments. The system leverages machine learning algorithms to analyze patient data collected from wearable devices and medical records, enabling early detection of health anomalies and timely intervention. Autonomous agents operate independently to monitor vital parameters, generate alerts, and provide personalized recommendations based on individual health profiles. Cloud infrastructure ensures scalability, data storage, and seamless communication between patients and healthcare providers. The proposed framework enhances decision-making, reduces response time, and improves overall patient care. Experimental results demonstrate improved accuracy, efficiency, and reliability in monitoring patient health conditions, making the system suitable for modern smart healthcare applications.