{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T06:46:27Z","timestamp":1776840387420,"version":"3.51.2"},"reference-count":63,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"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. Digit. Health"],"abstract":"<jats:p>Type 2 diabetes mellitus (T2DM) is associated with multi-organ complications, including cardiovascular and renal disease. Fundus photography provides a non-invasive window into systemic microvascular health, and artificial intelligence (AI) has enabled extraction of retinal biomarkers for systemic risk prediction beyond diabetic retinopathy detection. We conducted a methodologically structured scoping review following PRISMA-ScR guidance to map AI applications in retinal imaging for multi-organ risk stratification in T2DM. Studies using machine learning or deep learning models to predict cardiovascular, renal, or cerebrovascular outcomes were identified and characterized. Rather than quantitative pooling, we examined modeling strategies, validation approaches, performance reporting, and translational readiness across heterogeneous study designs. AI models frequently demonstrated promising discrimination; however, substantial heterogeneity was observed in cohort size, outcome definitions, imaging modalities, and validation strategies. External validation was limited, calibration was inconsistently assessed, and subgroup analyses addressing fairness and device-related domain shift were rarely reported. Most studies emphasized discrimination metrics without comprehensive evaluation of clinical utility.Retinal AI shows potential for scalable systemic risk surveillance in T2DM, but rigorous external validation, standardized reporting, and prospective implementation studies are required to enable safe and equitable clinical translation.<\/jats:p>","DOI":"10.3389\/fdgth.2026.1768780","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T05:48:44Z","timestamp":1776836924000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence in fundus photography for type 2 diabetes: a scoping review of systemic biomarkers and multi-organ risk prediction"],"prefix":"10.3389","volume":"8","author":[{"given":"Liting","family":"Huang","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Macau University of Science and Technology","place":["Taipa, Macao SAR, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyun","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Metabolism, Zhuhai People\u2019s Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University)","place":["Zhuhai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingqi","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Macau University of Science and Technology","place":["Taipa, Macao SAR, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai People's Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University)","place":["Zhuhai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mini","family":"Han Wang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Metabolism, Zhuhai People\u2019s Hospital (The Affiliated Hospital of Beijing Institute of Technology, Zhuhai Clinical Medical College of Jinan University)","place":["Zhuhai, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Macau University of Science and Technology","place":["Taipa, Macao SAR, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"04192","DOI":"10.7189\/jogh.14.04192","article-title":"Predicting vision-threatening diabetic retinopathy in patients with type 2 diabetes mellitus: systematic review, meta-analysis, and prospective validation study","volume":"14","author":"Liang","year":"2024","journal-title":"J Glob Health"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1016\/j.ophtha.2020.12.019","article-title":"Severity of diabetic retinopathy and the risk of future cerebrovascular disease, cardiovascular disease, and all-cause mortality","volume":"128","author":"Modjtahedi","year":"2021","journal-title":"Ophthalmology"},{"key":"B3","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1186\/s12911-023-02343-9","article-title":"The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients","volume":"23","author":"Shi","year":"2023","journal-title":"BMC Med Inform Decis Mak"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.3390\/bioengineering11111080","article-title":"HyMNet: a multimodal deep learning system for hypertension prediction using Fundus images and cardiometabolic risk factors","volume":"11","author":"Baharoon","year":"2024","journal-title":"Bioengineering"},{"key":"B5","doi-asserted-by":"publisher","first-page":"102927","DOI":"10.1016\/j.artmed.2024.102927","article-title":"Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers","volume":"154","author":"Messica","year":"2024","journal-title":"Artif Intell Med"},{"key":"B6","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s40123-022-00641-5","article-title":"Using artificial intelligence to analyse the retinal vascular network: the future of cardiovascular risk assessment based on oculomics? 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