A Review of Artificial Intelligence in Ophthalmology: Key Aspects, Challenges, and Future Directions

Abstract

Artificial intelligence (AI) is increasingly reshaping ophthalmology because the specialty depends heavily on structured imaging, quantitative measurements, and repeatable diagnostic workflows. This review provides a clinically grounded and translationally oriented synthesis of AI in ophthalmology, covering methodological foundations, ophthalmic imaging modalities, public datasets, disease-specific applications, evaluation metrics, deployment barriers, and future directions. Unlike reviews that mainly summarize algorithmic performance by disease category or model type, this article organizes ophthalmic AI through an integrated framework that emphasizes clinical use cases, evidence maturity, translational readiness, and real-world implementation requirements. The review examines applications across population screening, referral triage, disease grading, progression monitoring, prognosis, treatment guidance, workflow support, and automated reporting. Major disease domains include diabetic retinopathy, glaucoma, age-related macular degeneration, cataract, infectious keratitis, and keratoconus. Particular attention is given to the distinction between retrospective proof-of-concept studies, external validation, multicenter evaluation, prospective trials, and real-world deployment. The review also interprets evaluation metrics from a clinical perspective, highlighting the importance of threshold selection, sensitivity, specificity, false referral burden, missed disease, calibration, uncertainty, segmentation adequacy, robustness, and generalization. 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. Overall, the review argues that the next phase of ophthalmic AI should move beyond high accuracy values toward prospective validation, external generalization, clinician-centered design, calibrated uncertainty, and accountable integration into real-world eye-care pathways.