AI-Assisted Triage for Diabetic Retinopathy Integrated into Routine Diabetes Care: A Real-World Diagnostic Accuracy Study of the DAIRET® Algorithm

Background: Screening for diabetic retinopathy (DR) is resource-intensive. We evaluated the real-world diagnostic accuracy of the DAIRET® Artificial Intelligence (AI) algorithm, integrated into the electronic medical record, as a triage tool in routine diabetes care. Methods: Retrospective, STARD-AI-compliant study of consecutive adults with diabetes screened with non-mydriatic fundus photography between April and December 2025. The DAIRET® output (“suspected”/“not suspected”) was compared with grading by an ophthalmologist masked to the AI result. Sensitivity, specificity and predictive values with exact 95% confidence intervals were calculated for any DR and referable (moderate or worse) DR. Results: Among 1051 patients, 185 (17.6%) had DR: 160 mild and 25 moderate non-proliferative DR; none had severe DR. DAIRET® classified 271 patients (25.8%) as suspected. For any DR, sensitivity was 59.5% (52.0–66.6) and specificity 81.4% (78.7–83.9). For referable DR, sensitivity was 100.0% (86.3–100.0), specificity 76.0% (73.3–78.6) and negative predictive value 100.0% (99.5–100.0). Specificity was lower in patients aged <65 years and with type 1 diabetes. Conclusions: No referable case was missed, although sensitivity was based on 25 cases (lower limit 86.3%) and 15 AI-negative patients lacked complete verification. The 74% classified as “not suspected” is a potential, not measured, reduction in referrals. Performance for severe or proliferative DR could not be assessed, and multi-center confirmation is needed.