ArticleDiabetes, obesity & metabolism2026
Artificial Intelligence in Type 1 Diabetes Management: A Scoping Review of Randomised Controlled Trials.
Article in Diabetes, obesity & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence is emerging in healthcare systems. In type 1 diabetes, AI-enabled tools are increasingly used to support nutrition assessment and insulin decision-making, yet their clinical utility and safety remain unclear.
methodsThe study aims to identify and map the evidence on the clinical utility of AI-based diabetes management tools in people with type 1 diabetes. We conducted a scoping review following PRISMA-ScR guidelines, searching PubMed, CINAHL and Web of Science up to January 2026 for eligible randomised controlled trials.
resultsOur findings indicate that the evidence base is small and concentrated in high-income settings, with most trials assessing clinical utility using CGM outcomes and showing mixed improvements across interventions. No serious safety events were reported, but small sample sizes, short follow-up and inconsistent safety reporting limit confidence.
conclusionsFuture research should prioritise larger, longer-term real-world evaluations that use standardised safety endpoints and patient-centred outcomes, including in low- and middle-income countries to support equitable implementation.
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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 Socratic graph.