Artificial intelligence for diabetes care: current and future prospects
Abstract
Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
An AI agent can recall a patient's medical history, previous treatments, and responses to medications, helping clinicians to tailor more personalized treatment plans.
“For example, an AI agent can recall a patient’s medical history, previous treatments, and responses to medications, helping clinicians to tailor more personalized treatment plans.61”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [61]
The citation lists haven’t been fetched yet
References 213 · Cited by 106
- Authors
- Sheng, B., Pushpanathan, K., Guan, Z., Lim, Q. H., Lim, Z. W., Yew, S. M. E., Goh, J. H. L., Bee, Y. M., Sabanayagam, C., Sevdalis, N., Lim, C. C., Lim, C. T., Shaw, J., Jia, W., Ekinci, E. I., Simó, R., Lim, L.-L., Li, H., & Tham, Y.-C.
- Affiliations
- Shanghai Jiao Tong University (CN)
- National University of Singapore (SG)
- University of Malaya (MY)
- Singapore National Eye Center (SG)
- SingHealth (SG)
- Baker Heart and Diabetes Institute (AU)
- The University of Melbourne (AU)
- Instituto de Salud Carlos III (ES)
- Chinese University of Hong Kong (HK)
- Corresponding author
- Li, Huating
- Tham, Yih-Chung
- ORCID
- Venue
- The Lancet Diabetes & Endocrinology
- Publisher
- Elsevier BV
- Published
- 2024-08
- Volume
- 12
- Issue
- 8
- Pages
- 569-595
- ISSN
- 2213-8587 (ISSN-L)
- OpenAlex
- W4400901154
- Cited by
- 106
- References
- 213
- Citations by year
- 2026: 36 · 2025: 60 · 2024: 10
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- The Lancet Diabetes & Endocrinology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Excellent Young Scientists Fund (82022012)
- National Natural Science Foundation of China (62272298)
- National Medical Research Council
- Innovative Research Team of High-level Local University in Shanghai (SHSMU-ZDCX20212700)
- National Key Research and Development Program of China (2022YFA1004804)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, diabetes management, individualized care, algorithmic bias, AI ethics, health equity, diabetes screening, diabetes diagnosis, diabetes complications, complication prediction
- MeSH terms
- Artificial Intelligence, Diabetes Mellitus, Humans
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
