Integrating Artificial Intelligence and Context-Aware Modeling Protocols for Improved Patient-Centric Healthcare Systems

Abstract

The given paper proposes an innovative approach embedding the concept of Artificial Intelligence (AI) with that of context-aware modeling protocols as a means of facilitating patient-centric healthcare systems. The approach involves machine learning such as deep and reinforcement learning, augmented by real-time context data provided by IoMT sensors to perform intelligent, dynamically customized patient care. The framework enables predictive diagnostics, uninterrupted monitoring, and responsive treatment planning by adjusting healthcare responses to the context of specific patient based on factors such as physiological, environmental, and behavioral factors. Main research findings show that it achieved significant advances in accuracy, soundness and reaction times regarding conventional methods. Integrated system has a great potential of minimizing the number of clinical errors, enhancing good judgment, and eventually delivering smarter real time care to patients. Testings with artificial and actual health data sets validate the possibility of the system to stimulate compelling intelligent and patient-centered healthcare services.

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