ReviewEndocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists2025
Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.
Review in Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Postprandial Glycemic Control With Automated Insulin Delivery With Control-IQ Technology: The Impact of Meal Composition.Diabetes, obesity & metabolism · 2026Article
- Review
- Clinical Significance of Non-Invasive Skin Autofluorescence Measurement and AI Applications in Patients with Diabetic Foot Ulcers: A Scoping Review.Journal of personalized medicine · 2026Review
- Cross-fusion of digital twins and artificial intelligence in diabetes: from mechanistic elucidation to full-cycle precision management.Frontiers in endocrinology · 2026Review
- Telehealth for Integrated Cardiovascular and Diabetes Management: A Scoping Review.Journal of diabetes research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
backgroundArtificial 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.
methodsA 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.
findingsIn 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.