Application of AI-Driven Techniques for Smart Healthcare Monitoring Systems

Abstract

This paper explains the integration of advanced AI-driven techniques within the smart healthcare monitoring systems to significantly enhance the patient care for early diagnosis and real-time health management. In Existing methodologies, we propose a comprehensive AI-based framework that synergizes IoT sensor data analytics, machine learning models, and cloud-edge hybrid computing to enable continuous, personalized, and efficient health monitoring. Our approach explains the critical challenges such as data heterogeneity, latency, privacy, and interoperability by as a part of dynamic task allocation and secure data transmission protocols. The experimental results demonstrate the superior accuracy, responsiveness, and resource optimization which compared to conventional cloud-only or edge-only systems. This paper improves a scalable, secure, and patient-centric solution, for future clinical adoption and integration with electronic health records and federated learning models.