Synergistic AI-Resident Approach Achieves Superior Diagnostic Accuracy in Tertiary Ophthalmic Care for Glaucoma and Retinal Disease

Dalia Camacho^1*Gabriela Baylón-Vázquez²Karen Arriozola-Rodríguez²Luis Enrique Avalos-Ramírez²Curt Hartleben-Matkin²Hugo Francisco Valdez-Flores³Damaris Hodelin-Fuentes⁴Alejandro Noriega Campero¹

• ¹PROSPERiA, Mexico City, Mexico
• ²Instituto de Oftalmología Fundación de Asistencia Privada Conde de Valenciana, I.A.P, Mexico, Mexico City, Mexico
• ³Hospital de Nuestra Señora de la Luz, Mexico City, Mexico
• ⁴Afiliado al hospital juan Bruno zayas, Santiago de Cuba, Santiago de Cuba, Cuba

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.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 suspected glaucoma classification. In addition, we evaluated a synergistic approach combining AI and resident assessments.Results: For suspect glaucoma 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 achieved a higher sensitivity (80.4%) than ophthalmic residents alone or AI alone (p < 0.001). AI's 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 the 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).1 Camacho-García-Formentí et al.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.