Because ophthalmology depends heavily on structured imaging, quantitative measurements, and repeatable diagnostic workflows, it has become a leading field for AI application. Rather than summarizing algorithmic performance by disease category or model type, this review organizes ophthalmic AI through an integrated framework emphasizing clinical use cases, evidence maturity, translational readiness, and real-world implementation requirements. Applications span population screening, referral triage, disease grading, progression monitoring, prognosis, treatment guidance, workflow support, and automated reporting, across diseases including diabetic retinopathy, glaucoma, age-related macular degeneration, cataract, infectious keratitis, and keratoconus.

The review distinguishes retrospective proof-of-concept studies, external validation, multicenter evaluation, prospective trials, and real-world deployment, and interprets evaluation metrics—such as threshold selection, sensitivity, specificity, false referral burden, missed disease, calibration, and uncertainty—from a clinical perspective. Key translational challenges include dataset bias, domain shift, interpretability, privacy, regulatory oversight, infrastructure constraints, workflow integration, and post-deployment monitoring. Emerging paradigms such as multimodal AI, foundation models, generative AI, and edge-based point-of-care systems are discussed cautiously, with emphasis on hallucination risk, clinical grounding, accountability, and the gap between benchmark performance and deployment readiness. The authors conclude that ophthalmic AI must move beyond high accuracy values toward prospective validation, external generalization, clinician-centered design, calibrated uncertainty, and accountable integration into real-world eye-care pathways.

The work titled “A Review of Artificial Intelligence in Ophthalmology: Key Aspects, Challenges, and Future Directions”, was published on Eye & ENT Research (published on July 14, 2026).