{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T11:27:10Z","timestamp":1786706830700,"version":"build-2736575974"},"reference-count":127,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:p>Prediabetes is a highly prevalent intermediate metabolic state and a major public health target for preventing type 2 diabetes mellitus (T2DM). However, current approaches to prediabetes screening, risk stratification, and lifestyle management remain limited by inconsistent diagnostic definitions, incomplete case detection, heterogeneous progression risk, and the resource-intensive nature of conventional face-to-face prevention programmes. Artificial intelligence (AI), including machine learning, deep learning, explainable AI, and algorithm-driven digital interventions, is increasingly being explored as a tool to address these gaps. This narrative review used a structured search of PubMed, Web of Science Core Collection, and Embase for studies published from January 2010 to April 2026, selecting articles that evaluated AI-assisted or algorithm-driven approaches for prediabetes screening, progression risk prediction, or lifestyle intervention and reported relevant model performance, validation, or intervention outcomes. Current evidence indicates that AI-based models can improve discrimination beyond traditional risk scores by integrating routine clinical data, longitudinal electronic health records, continuous glucose monitoring profiles, wearable-derived behavioural signals, and emerging molecular biomarkers. Some externally validated models have shown clinically relevant performance for identifying individuals at high risk of progression and for guiding more targeted preventive strategies. In parallel, fully or semi-automated digital programmes delivered through mobile applications, web platforms, connected scales, and sensor-based feedback systems have demonstrated potential to support lifestyle change, improve engagement, and reduce reliance on labour-intensive counselling. Nevertheless, translation into routine care remains constrained by heterogeneity in prediabetes definitions, limited external validation across diverse populations, uncertain long-term effectiveness, geographical imbalance in evidence, privacy concerns, and the need for stronger governance frameworks. Overall, AI should be viewed as an assistive technology that may support earlier detection, more precise risk stratification, and scalable lifestyle management in prediabetes. Further multi-centre, prospective, and implementation-focused studies are needed to establish clinical utility, equity, safety, and cost-effectiveness.<\/jats:p>","DOI":"10.3389\/fendo.2026.1885905","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T10:29:06Z","timestamp":1786703346000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in prediabetes care: applications in screening, risk prediction, and lifestyle intervention"],"prefix":"10.3389","volume":"17","author":[{"given":"Runxuan","family":"Chen","sequence":"first","affiliation":[{"name":"School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Neurology, Qilu Hospital of Shandong University (Qingdao)","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tong","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of General Medicine, Qilu Hospital of Shandong University (Qingdao)","place":["Qingdao, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.2337\/dc22-2376","article-title":"Global prevalence of prediabetes","volume":"46","author":"Rooney","year":"2023","journal-title":"Diabetes Care"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(23)01301-6","article-title":"Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 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