{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:43:59Z","timestamp":1785545039932,"version":"3.56.0"},"reference-count":36,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Res Protoc"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Diabetic retinopathy (DR) and age-related macular degeneration (AMD) are 2 of the leading causes of vision loss worldwide. As population aging and diabetes prevalence increase, timely detection of these conditions has become essential. However, limited professionalism and insufficient training in ophthalmic screening among general medicine physicians may lead to delayed diagnosis and treatment. Artificial intelligence (AI)\u2013assisted diagnostic tools may help to improve the screening of DR and AMD in routine clinical practice.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to evaluate the clinical effectiveness and cost-effectiveness of AI-assisted fundus imaging for DR and AMD screening in adults with diabetes and older adults at risk of macular degeneration.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This multicenter, 2-arm, parallel-group, open-label, individual-level randomized controlled trial and patient recruitment are performed at the settings of Family Medicine and Geriatric and Gerontology Care over 4 medical centers in Taiwan. Eligibility includes (1) diabetic individuals aged \u226520 years for DR screening, and (2) individuals aged \u226550 years for AMD screening. The study protocol has been approved by the ethics committees of all participating hospitals, and all participants will provide written informed consent.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The study was funded in September 2024, began on October 2, 2025, and is expected to be completed in December 2027. After the pilot implementation phase without randomization, participants will be randomized 1:1 into two groups: (1) AI-assisted screening, and (2) usual physician-only screening. The primary outcomes will include the detection rates (defined as participants with confirmed DR or AMD among all screened participants) and the positive predictive values (defined as participants with confirmed DR or AMD among those who tested positive). Cost-effectiveness analyses will be performed using data derived from the trial results.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study will provide robust evidence on the effectiveness of AI-assisted ophthalmic screening in improving patient eye health outcomes through timely screening and accurate early detection. This strategy may be cost-effective.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/91699","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T09:20:05Z","timestamp":1779873605000},"page":"e91699-e91699","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence\u2013Assisted Screening for Patients With Diabetic Retinopathy and Age-Related Macular Degeneration in Family Medicine and Geriatric and Gerontology Care: Protocol for a Pragmatic Randomized Clinical 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