ArticleJAMA network open2025
Diabetic Retinopathy Screening Among Federally Qualified Health Center Patients Using Point-of-Care AI: DRES-POCAI: A Trial Protocol.
Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06721351 (Diabetic Retinopathy Screening Point-of-Care Artificial Intelligence -DRES-POCAI), which is not on this map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Diabetic Retinopathy Screening Point-of-Care Artificial Intelligence -DRES-POCAI: AI - Clinical Intervention at San Ysidro Health
Who cites it
3 citing papers in PubMed.
- Artificial Intelligence-Assisted 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 Trial.JMIR research protocols · 2026Article
- AI-induced never-skilling in medical education.Nature medicine · 2026Review
- Validation of the Eyerobo FC Portable Fundus Camera for Diabetic Retinopathy Screening Using Public Datasets and Deep Learning.Ophthalmology and therapy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Diabetic retinopathy screening (DRS) rates have historically been low among underserved populations due to barriers in accessing traditional eye care. Although artificial intelligence (AI)-powered DRS provides a potential strategy to improve screening rates, its optimal integration into primary care workflows within federally qualified health centers (FQHCs) requires rigorous evaluation. The clinical workflow of the Diabetic Retinopathy Screening Point-of-Care Artificial Intelligence (DRES-POCAI) trial in FQHCs integrates AI-powered DRS with electronic health records (EHRs) to automate results and prompt referrals, aiming to improve screening rates and facilitate early diagnosis and timely treatment. Objective: To increase DRS rates, facilitate early-stage DR detection, improve timely eye specialist follow-up, and assess the effect of DRS on patients' knowledge, attitudes, self-efficacy, and satisfaction. Design, Setting, and Participants: DRES-POCAI is a patient-level, multiclinic, open-label, parallel superiority randomized clinical trial at 2 FQHC sites of San Ysidro Health in San Diego County, California. The study recruitment targets 848 active FQHC patients aged 22 years or older with diabetes, no DRS in the prior 11 months, and scheduled medical visits during the intervention period. Patients with a history of retinopathy or retinal vascular occlusion and other physical or mental conditions are excluded. The study started in June 2024, with recruitment anticipated to conclude in August 2025 and follow-up until February 2026. Intervention: The intervention arm receives DRS at their primary care clinic using an AI-powered DRS system, with retinal image analysis to identify more than mild DR and vision-threatening DR. Results are immediately available in the EHRs, and practitioners receive risk-stratified referral recommendations. The usual care arm receives referrals to an FQHC optometrist or external eye care practitioner, with results transmitted to the medical home later. Main Outcomes and Measures: The primary outcome is DRS completion status. Secondary outcomes include DR diagnosis stage, specialist referrals, and participants' DR knowledge, attitudes, and intentions regarding future AI-powered DRS. Results: Findings will be disseminated in peer-reviewed publications after data collection and analysis. Conclusions and Relevance: DRES-POCAI will determine the effectiveness of an AI-powered DRS intervention to increase DRS rates in FQHC primary care workflows. Trial Registration: ClinicalTrials.gov Identifier: NCT06721351.
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Registered trials
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