Published Paper Details:

The integration of Artificial Intelligence (AI) into healthcare is reshaping the delivery of medical services, enabling early disease detection, precise treatment planning, and real-time monitoring. This research proposes an AI-driven framework for intelligent healthcare and predictive diagnostics that leverages machine learning, deep learning, and natural language processing to extract actionable insights from heterogeneous medical data, including electronic health records, imaging, and sensor-based monitoring systems. The framework emphasizes predictive modeling to forecast disease progression, support preventive interventions, and personalize treatment strategies while ensuring scalability across diverse clinical scenarios. Key contributions include the design of adaptive algorithms capable of handling high-dimensional data, mechanisms for explainable decision-making to enhance trust among clinicians, and integration with cloud-edge infrastructures for timely and resource-efficient deployment. Experimental validation highlights improved diagnostic accuracy, reduced latency in decision support, and enhanced patient outcomes compared to conventional approaches. This work underscores the transformative potential of AI in advancing predictive diagnostics, fostering proactive healthcare, and paving the way toward sustainable, patient-centered medical ecosystems.