Artificial Intelligence and Precision Medicine in the Early Detection of Ocular Diseases

Resumen

Ocular diseases such as diabetic retinopathy (DR), glaucoma, and age-related macular degeneration (AMD) remain leading causes of preventable blindness worldwide. Early detection is critical, yet access to timely ophthalmologic evaluation is limited in many regions. This study evaluated the diagnostic accuracy and healthcare impact of artificial intelligence (AI) models, alone and combined with clinical and genomic data, across diverse Latin American populations. A total of 6,500 adults from Mexico, Colombia, and Ecuador underwent fundus photography, optical coherence tomography (OCT), and polygenic risk score (PRS) analysis, with data analyzed using convolutional neural networks and transformer architectures. The results showed that AI achieved strong diagnostic performance (AUC: DR 0.94, glaucoma 0.92, AMD 0.90), with improvements when clinical and genomic data were integrated (AUC up to 0.96 for DR and 0.95 for glaucoma). Subgroup analyses confirmed robustness across age and sex, although performance was lower in low socioeconomic groups. AI-assisted referral pathways reduced waiting times from 45 to 27 days, and performance was consistent across Mexico, Colombia, and Ecuador, with minor variations reflecting healthcare infrastructure. These findings demonstrate that AI combined with precision medicine can significantly improve early detection, enhance referral efficiency, and support equitable multinational deployment, reinforcing its potential as a transformative tool to reduce preventable blindness.