A Patient-Centered Evaluation of AI in Healthcare for Diabetes
Abstract
Artificial Intelligence (AI) is transforming chronic disease management by enabling predictive analytics, personalized treatment, and continuous remote monitoring. Diabetes mellitus, one of the most prevalent chronic diseases globally, demands real-time decision support and patient engagement— needs often unmet by conventional healthcare systems. This paper presents a patient-centered evaluation of an AI-enabled diabetes man-agement framework designed to predict glycemic fluctuations, generate personalized insights, and support treatment decisions through a unified digital platform. The system integrates machine learning models, electronic health records, wearable sensor data, and patient-reported logs to deliver individualized recommenda-tions. A modular architecture incorporating prediction engines, clinician dashboards, and a patient mobile interface was im-plemented using Python, Node.js, MongoDB, and deep-learning libraries. Experimental results demonstrate high prediction ac-curacy (93.5%), low processing latency (1.8 seconds), strong usability (90% task-completion rate), and high user satisfaction (4.6/5). The study highlights that patient trust, interpretability, and ease of use are critical enablers of AI adoption in healthcare. This work contributes a structured methodology and empirical findings supporting scalable, humancentered AI applications for global diabetes care.
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