{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:08:21Z","timestamp":1778494101677,"version":"3.51.4"},"reference-count":12,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,11]]},"DOI":"10.7759\/cureus.108630","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T09:10:31Z","timestamp":1778490631000},"source":"Crossref","is-referenced-by-count":0,"title":["Beyond the Model: Evaluating AI Decision Strategies for Diabetic Retinopathy Screening Under Clinical Constraints"],"prefix":"10.7759","author":[{"given":"Shlok S","family":"Jena","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madan M","family":"Rayguru","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"ref1","article-title":"IDF Diabetes Atlas [Internet]. 10th Edition","author":"Magliano DJ","year":"2021","unstructured":"Magliano DJ, Boyko EJ. IDF Diabetes Atlas [Internet]. 10th Edition. International Diabetes Federation, Brussels; 2021."},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan V","year":"2016","unstructured":"Gulshan V, Peng L, Coram M, et al.. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016, 316:2402-10. 10.1001\/jama.2016.17216","journal-title":"JAMA"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting DS","year":"2017","unstructured":"Ting DS, Cheung CY, Lim G, et al.. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017, 318:2211-23. 10.1001\/jama.2017.18152","journal-title":"JAMA"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Abr\u00e0moff MD","year":"2018","unstructured":"Abr\u00e0moff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018, 1:39. 10.1038\/s41746-018-0040-6","journal-title":"NPJ Digit Med"},{"key":"ref5","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy A","year":"2021","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al.. An image is worth 16x16 words: transformers for image recognition at scale. International Conference on Learning Representations (ICLR). 2021."},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376718","article-title":"A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy","author":"Beede E","year":"2020","unstructured":"Beede E, Baylor E, Hersch F, et al.. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020. 1-12. 10.1145\/3313831.3376718"},{"key":"ref7","article-title":"On calibration of modern neural networks","author":"Guo C","year":"2017","unstructured":"Guo C, Pleiss G, Sun Y, Weinberger KQ. On calibration of modern neural networks. Proceedings of the 34th International Conference on Machine Learning. 2017. 1321-30."},{"key":"ref8","article-title":"Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift","author":"Ovadia Y","year":"2019","unstructured":"Ovadia Y, Fertig E, Ren J, et al.. Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift. Advances in Neural Information Processing Systems 32. 2019. 13969-80."},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1186\/s40662-020-00182-7","article-title":"Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review","volume":"7","author":"Lim G","year":"2020","unstructured":"Lim G, Bellemo V, Xie Y, Lee XQ, Yip MY, Ting DS. Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: a review. Eye Vis (Lond). 2020, 7:21. 10.1186\/s40662-020-00182-7","journal-title":"Eye Vis (Lond)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(22)00017-6","article-title":"Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study","volume":"1016","author":"Ruamviboonsuk P","year":"2022","unstructured":"Ruamviboonsuk P, Krause J, Chotcomwongse P, et al.. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health. 2022, 1016:2589-7500. 10.1016\/S2589-7500(22)00017-6","journal-title":"Lancet Digit Health"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.3389\/fendo.2023.1197783","article-title":"Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies","volume":"14","author":"Wang Z","year":"2023","unstructured":"Wang Z, Li Z, Li K, Mu S, Zhou X, Di Y. Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies. Front Endocrinol (Lausanne). 2023, 14:1197783. 10.3389\/fendo.2023.1197783","journal-title":"Front Endocrinol (Lausanne)"},{"key":"ref12","unstructured":"Diabetic retinopathy detection. https.\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection."}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/489279-beyond-the-model-evaluating-ai-decision-strategies-for-diabetic-retinopathy-screening-under-clinical-constraints","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T09:10:36Z","timestamp":1778490636000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/489279-beyond-the-model-evaluating-ai-decision-strategies-for-diabetic-retinopathy-screening-under-clinical-constraints"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,11]]},"references-count":12,"URL":"https:\/\/doi.org\/10.7759\/cureus.108630","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,11]]}}}