Synergistic AI-resident approach achieves superior diagnostic accuracy in tertiary ophthalmic care for glaucoma and retinal disease.

INTRODUCTION

Artificial intelligence (AI) shows promise in ophthalmology, but its potential in tertiary care settings in Latin America remains understudied. We present a Mexican AI-powered screening tool and evaluate it against first-year ophthalmology residents in a tertiary care setting in Mexico City.

METHODS

We analyzed data from 435 adult patients undergoing their first ophthalmic evaluation using an AI-based platform and first-year ophthalmology residents. The platform employs an Inception V3-based multi-output classification model with 512 × 512 input resolution to capture small lesions when detecting retinal disease. To evaluate glaucoma suspects, the system uses U-Net models that segment the optic disc and cup to calculate cup-to-disc ratio (CDR) from their vertical heights. The AI and resident evaluations were compared with expert annotations for retinal disease, CDR measurements, and glaucoma suspect classification. In addition, we evaluated a synergistic approach combining AI and resident assessments.

RESULTS

For glaucoma suspect classification, AI outperformed residents in accuracy (88.6% vs. 82.9%, = 0.016), sensitivity (63.0% vs. 50.0%, = 0.116), and specificity (94.5% vs. 90.5%, = 0.062). The synergistic approach achieved a higher sensitivity (80.4%) than ophthalmic residents alone or AI alone ( 0.001). AI's CDR estimates showed lower mean absolute error (0.056 vs. 0.105, 0.001) and higher correlation with expert measurements ( = 0.728 vs. = 0.538). In the retinal disease assessment, AI demonstrated higher sensitivity (90.1% vs. 63.0% for medium/high risk, 0.001) and specificity (95.8% vs. 90.4%, 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).

DISCUSSION

AI outperformed first-year residents in key ophthalmic assessments. The synergistic use of AI and resident assessments showed potential for optimizing diagnostic accuracy, highlighting the value of AI as a supportive tool in ophthalmic practice, especially for early career clinicians.