Comparative Performance of retinIA, an AI-powered Ophthalmic Screening Tool, and First-Year Residents in Retinal Disease Detection and Glaucoma Assessment: A Study in a Mexican Tertiary Care Setting

BackgroundArtificial intelligence (AI) shows promise in ophthalmology, but its potential on tertiary care settings in Latin America remains understudied. We evaluated a Mexican AI-powered screening tool, against first-year ophthalmology residents in a tertiary care setting in Mexico City. MethodsWe analysed 435 adult patients undergoing their first ophthalmic evaluation. AI and residents assessments were compared against expert annotations for retinal disease, cup-to-disk ratio (CDR) measurements, and glaucoma suspect classification. We also evaluated a synergistic approach combining AI and resident assessments. ResultsFor glaucoma suspect classification, AI outperformed residents in accuracy (88.6% vs 82.9%, p = 0.016), sensitivity (63.0% vs 50.0%, p = 0.116), and specificity (94.5% vs 90.5%, p = 0.062). The synergistic approach deemed a higher sensitivity (80.4%) than ophthalmic residents alone or AI alone (p < 0.001). AIs CDR estimates showed lower mean absolute error (0.056 vs 0.105, p < 0.001) and higher correlation with expert measurements (r = 0.728 vs r = 0.538). In retinal disease assessment, AI demonstrated higher sensitivity (90.1% vs 63.0% for medium/high-risk, p < 0.001) and specificity (95.8% vs 90.4%, p < 0.001). Furthermore, differences between AI and residents were statistically significant across all metrics. The synergistic approach achieved the highest sensitivity for retinal disease (92.6% for medium/high-risk, 100% for high-risk). ConclusionAI outperforms first-year residents in key ophthalmic assessments. The synergistic use of AI and resident assessments shows potential for optimizing diagnostic accuracy, highlighting the value of AI as a supportive tool in ophthalmic practice, especially for early-career clinicians.