AI-Based Predictive Models for Effective Diabetes Management

Abstract

AI and ML have emerged as transformative tools in the management of diabetes, enabling early detection, risk prediction, personalized therapy, and improved patient adherence. Recent studies prove that AI can analyze huge datasets, including electronic health records, continuous glucose monitoring, lifestyle input, and biomarker profiles, for the identification of patients who have a high risk of developing type 2 diabetes and its complications. Various predictive models have been proposed with supervised learning, deep learning, ensemble methods, and time-series analysis, which have performed well in predicting glycemic events, treatment response, and comorbidity risk. Moreover, AI-driven systems enable personalized interventions, optimize insulin dosing, dietary planning, and continuous monitoring for precision care in diabetes. However, there are considerable challenges regarding model generalizability, integration into clinical workflows, data privacy, and real-world implementation. Systematic reviews present the potential of AI to enhance decision-making while highlighting robust validation, standardized datasets, and interdisciplinary collaboration. As the use of AI-based solutions gains momentum in healthcare systems, these technologies will improve outcomes and decrease complications while enabling proactive management of diabetic populations worldwide. Further research efforts should be directed toward scalable, ethically responsible frameworks to guarantee equity in access and seamless integration into routine clinical practice.

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