Artificial Intelligence in Ophthalmology: From Methodological Advances to Clinical Translation and Future Directions

Abstract

Artificial intelligence (AI) is reshaping ophthalmology from task‐specific image analysis toward multimodal, longitudinal, and clinically integrated decision support. This narrative review summarizes the methodological evolution of ophthalmic AI, including traditional machine learning, task‐specific deep learning, self‐supervised learning, foundation models, multimodal AI, and generative AI, and examines their applications across major ophthalmic diseases. Current evidence is strongest for autonomous diabetic retinopathy screening, whereas AI applications in glaucoma, age‐related macular degeneration, anterior segment disease, ocular surface disorders, and ocular tumors are primarily positioned as decision‐support tools for screening, diagnosis, risk stratification, progression prediction, treatment monitoring, and quantitative imaging analysis. Recent advances in ophthalmic foundation models, multimodal learning, three‐dimensional optical coherence tomography analysis, and generative AI have expanded the scope of AI beyond single‐modality classification, but improved benchmark performance does not necessarily indicate clinical readiness. Successful translation requires robust external and prospective validation, reliable uncertainty estimation, cross‐device and cross‐population generalizability, fairness, interpretability, privacy protection, safety, and effective human–AI collaboration. Emerging directions include AI‐generated virtual patients and digital twins, home and portable imaging for decentralized monitoring, AI‐driven imaging biomarker discovery, oculomics for systemic health assessment, and interactive clinical copilots. Overall, the future value of ophthalmic AI will depend not only on increasingly powerful models but also on their ability to provide reproducible, clinically actionable, and trustworthy benefits within real‐world clinical workflows.