Artificial Intelligence-Assisted Interpretation of Ophthalmic Images for Early Detection of Retinal Diseases

Retinal diseases constitute major causes of avoidable visual impairment, with early manifestations that are frequently subtle, spatially distributed, and challenging to interpret consistently, particularly in busy or under-resourced clinical settings. Artificial intelligence-assisted interpretation of ophthalmic images enables earlier recognition by transforming fundus photographs, optical coherence tomography, optical coherence tomography angiography, and multimodal records into reproducible estimates of disease presence, severity, and referral urgency. This review thoroughly examines evidence published from 2020 to 2025 regarding image-based artificial intelligence for early detection of diabetic retinopathy, age-related macular degeneration, glaucoma, retinal vascular occlusion, and inherited retinal degeneration. In contrast to broad reviews of artificial intelligence in ophthalmology, this work focuses on the entire early-detection pathway, including image acquisition, quality control, lesion localization, diagnostic classification, uncertainty communication, clinical triage, and longitudinal monitoring. A structured narrative review was carried out using PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect, employing targeted searches that combined retinal disease terms, imaging modalities, artificial intelligence methods, screening, validation, explainability, and clinical deployment. Evidence was synthesized with respect to diagnostic performance, transportability, workflow value, and patient-safety requirements. While deep learning systems are able to achieve high discrimination in selected datasets, their real-world value depends on image quality, spectrum diversity, external validation, calibration, interoperable reporting, and clinically meaningful thresholds. Fundus photography remains highly scalable for population screening, whereas optical coherence tomography and multimodal models provide more comprehensive structural and temporal information for macular and glaucomatous disease. The review concludes that artificial intelligence should serve as a supervised interpretation layer rather than an autonomous replacement for clinical judgment. Upcoming studies must prioritize prospective multicenter evaluation, subgroup fairness, uncertainty-aware outputs, privacy-preserving learning, and outcome-based comparisons that assess earlier treatment, preserved vision, and reduced inequity rather than accuracy alone.