REMEDIS: A Clinical AI Framework for Retinal Disease Diagnosis with Explainable Fundus Image Analysis - Lee, Youngkyu; Lee, Jinho; Jo, Youngdae; Park, Jeongwoo (Proceedings of the AAAI Conference on Artificial Intelligence, 2026)
Timely detection of retinal diseases is crucial for preventing vision loss; yet the limited availability of ophthalmologists and disparities in access to diagnostic services continue to hinder widespread screening, particularly in primary care settings. We present REMEDIS, a Software-as-a-Service (SaaS)-based clinical AI framework for the automated diagnosis of major retinal diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), epiretinal membrane (ERM), and glaucoma, using fundus images. The system analyzes high-resolution fundus photographs in a secure cloud environment via a Swin-Large-based multi-disease classification network, producing disease-specific probability scores. To ensure clinically meaningful decision making, Youden’s Index is applied to determine optimized sensitivity-specificity thresholds for each condition. An explainability module based on Grad-CAM generates lesion localization contour visualizations, providing interpretable evidence that assists ophthalmologists in case review and facilitates integration into electronic medical records (EMR). The framework was evaluated in an IRB-approved multicenter prospective clinical trial conducted under real-world conditions, achieving an average AUC exceeding 0.94 across the four target diseases and demonstrating strong concordance with expert diagnoses. To our knowledge, this represents one of the first SaaS-based AI diagnostic frameworks for retinal diseases validated through prospective clinical studies, highlighting its potential as an emerging clinical application of AI.