{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T13:09:08Z","timestamp":1791896948905,"version":"4.3.4"},"reference-count":31,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Screening for retinal disease is increasingly performed by general practitioners and other non-ophthalmologist clinicians in primary care, especially where access to ophthalmology is limited and diagnostic accuracy may be suboptimal. To investigate the role of an automated fundus-interpretation support solution in improving general physicians\u2019 screening accuracy and referral decisions, we conducted a paired before\u2013after study evaluating an AI-based decision support tool. Four non-ophthalmologists who have been involved in screen fundus images in clinical practice reviewed 500 de-identified color fundus photographs twice\u2014first unaided and, after a washout period, with AI assistance. With AI support, diagnostic accuracy improved significantly from 82.8% to 91.1% (p &lt; 0.0001), with the greatest benefit observed in glaucoma-suspect and multi-pathology cases. Clinicians retained final diagnostic authority, and a favorable safety profile was observed. These results demonstrate that AI-assisted diagnosis aid can meaningfully augment non-ophthalmologist screening and referral decision-making in real-world primary care, while underscoring the need for broader validation and implementation studies.<\/jats:p>","DOI":"10.3390\/bioengineering12121304","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T16:31:52Z","timestamp":1764261112000},"page":"1304","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["AI-Augmented Fundus Disease Screening by Non-Ophthalmologist Physicians: A Paired Before\u2013After Study"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4026-3502","authenticated-orcid":false,"given":"EunAh","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon 51353, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2110-1874","authenticated-orcid":false,"given":"Su Jeong","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea"},{"name":"Biomedical Institute for Convergence (BICS), Sungkyunkwan University, Suwon 16419, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.ajo.2021.03.029","article-title":"Clinic-Based Eye Disease Screening Using Non-Expert Fundus Photo Graders at the Point of Screening: Diagnostic Validity and Yield","volume":"227","author":"Ausayakhun","year":"2021","journal-title":"Am. 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