Artificial intelligence in type 1 diabetes management: a scoping review of randomised controlled trials
Author(s)
Wu, Yucen
Pang, Jun
Xu, Shaoyong
Leelarathna, Lalantha
Lett, Aaron M
Type
Journal Article
Abstract
Background
Artificial 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.
Methods
The 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.
Results
Our 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.
Conclusions
Future 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.
Artificial 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.
Methods
The 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.
Results
Our 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.
Conclusions
Future 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.
Date Issued
2026-06-01
Date Acceptance
2026-03-06
Citation
Diabetes, Obesity and Metabolism, 2026, 28 (6), pp.4860-4875
ISSN
1462-8902
Publisher
Wiley
Start Page
4860
End Page
4875
Journal / Book Title
Diabetes, Obesity and Metabolism
Volume
28
Issue
6
Copyright Statement
© 2026 The Author(s). Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41852263
Subjects
artificial intelligence
diabetes management
randomised controlled trials (RCTs)
safety
type 1 diabetes
Humans
Diabetes Mellitus, Type 1
Artificial Intelligence
Randomized Controlled Trials as Topic
Hypoglycemic Agents
Insulin
Publication Status
Published
Coverage Spatial
England
Date Publish Online
2026-03-19