Precision in an Era of Complexity: Integrating AI with Novel Anti Diabetic Drugs for Personalized Diabetes Management

Abstract

The global burden of diabetes mellitus continues to rise, presenting complex challenges in early diagnosis, individualized treatment, and long-term disease management. Concurrently, the therapeutic landscape has evolved significantly with the advent of novel anti-diabetic agents, such as GLP-1 receptor agonists, SGLT2 inhibitors, and dual/triple agonists that offer benefits beyond glycemic control, including cardiovascular and renal protection. In this context, artificial intelligence (AI) has emerged as a transformative force in endocrinology, offering data-driven solutions across the entire spectrum of diabetes care. This narrative review explores the integration of AI technologies—including machine learning, deep learning, reinforcement learning, and explainable AI with contemporary diabetes management strategies. It examines the role of AI in early risk prediction and diagnosis through electronic health records, medical imaging, and biomarker discovery; its application in personalizing treatment regimens, optimizing insulin delivery, and supporting medication adherence; and its utility in real-time monitoring via continuous glucose monitoring systems and wearable technologies. Furthermore, the review discusses how AI is being leveraged to enhance the development, selection, and post-marketing surveillance of novel anti-diabetic pharmacotherapies. By synthesizing evidence, this article provides a comprehensive overview of current methodologies, technological synergies, and conceptual frameworks that underpin the convergence of AI and modern diabetes therapeutics.

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