Impact of Artificial Intelligence on Geriatric Clinical Care for Chronic Diseases: Integrating Time-Series Analysis, Wearable Health Devices, CDSS, Medical Image Analysis, and Voice-Based Diagnostics

The rapidly aging population presents significant challenges in managing chronic diseases. This study explores an integrated AI-driven framework utilizing time-series analysis, wearable health devices, clinical decision support systems (CDSS), medical image analysis, and voice-based diagnostics to revolutionize geriatric care. The research focuses on predictive health monitoring, real-time data collection, evidence-based decision-making, and non-invasive diagnostics, aiming to enhance healthcare outcomes and simplify elderly care management. The proposed framework demonstrated superior performance, achieving 94% accuracy, 92% scalability, and a 95% F1 score compared to traditional methods. This scalable and efficient approach offers innovative, patient-centric solutions for geriatric healthcare by addressing the complexities of chronic disease management.