ReviewJournal of diabetes and metabolic disorders2025
Revolutionizing diabetes care: the role of artificial intelligence in prevention, diagnosis, and patient care.
Review in Journal of diabetes and metabolic disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Continuous glucose monitoring in paediatric residents with normal glucose metabolism: correlations to 24-hour duty schedule.Journal of diabetes and metabolic disorders · 2026Article
- Artificial intelligence-driven therapeutics for disease modification in type 1 diabetes: a digital public health and clinical translation framework.Frontiers in public health · 2026Review
- A multi-omics Mendelian randomization study reveals PAM as a potential therapeutic target for type 2 diabetes.Journal of translational medicine · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Millions of people worldwide have diabetes, a disease that is becoming more common and has substantial socioeconomic costs. Artificial intelligence (AI) improves diabetes management, diagnosis, and prevention. AI-powered tools enable early detection of diabetes and its complications, including diabetic retinopathy, using sophisticated algorithms and large-scale data analysis. Wearable devices and continuous glucose monitors, integrated with AI, facilitate personalized treatment plans and real-time insights, improving glycemic control and overall health outcomes. Advanced machine learning models demonstrate high accuracy in diagnosing and predicting diabetes, while automated insulin delivery systems and bolus calculators enhance insulin management, reducing risks of hypo- and hyperglycemia. Despite these advancements, challenges such as cost, accessibility, device interoperability, and ethical considerations persist. The development of new digital biomarkers, individualized clinical metrics, and patient-centric solutions is critical for optimizing care. While AI holds immense promise in alleviating the global diabetes burden, addressing these limitations through sustained innovation and collaboration is essential. This review underscores the transformative potential of AI in revolutionizing diabetes care, enabling advancement for enhanced prevention, precise diagnosis, and effective management strategies. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-025-01648-y.
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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 OpenQuestion graph.