Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.

BACKGROUND

Artificial intelligence (AI) is rapidly transforming clinical medicine, and its impact on diabetes care is especially noteworthy. By enhancing diagnostic accuracy and optimizing treatment strategies, AI can reduce patient burden and improve quality of life. In this narrative review, we examine the latest AI applications in diabetes care, exploring their capabilities, limitations, and the future directions needed to fully translate these advances into routine practice.

METHODS

A comprehensive search of PubMed, Google Scholar, and ScienceDirect identified relevant articles focused on the use of AI and machine learning (ML) in diabetes care. To enrich the evidence base, we also incorporated emerging approaches from the research programs of the contributing authors. Key findings from these studies were extracted and synthesized to highlight emerging trends, applications, and outcomes.

FINDINGS

In recent years, both traditional ML approaches and deep learning algorithms have been applied to improve screening for complications of diabetes such as retinopathy, macular edema, and neuropathy, predict disease progression risk, and enhance clinical decision support systems for diagnosis, prognosis, and treatment optimization. AI-driven solutions are also emerging to identify noninvasive biomarkers for detecting diabetes and prediabetes, analyze the macronutrient content of meals using image-based deep learning methods, integrate novel risk prediction tools within electronic health records, and optimize automated insulin delivery systems.

IMPLICATIONS

AI advancements hold promise for streamlining patient care, personalizing treatment plans, and ultimately improving clinical outcomes for individuals living with diabetes.