Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.

Abstract

Background: By leveraging the imaging-rich nature of ophthalmology and optometry, artificial intelligence (AI) is rapidly transforming the vision sciences and addressing the global burden of ocular diseases. The ability of AI to analyze complex imaging and clinical data allows unprecedented improvements in diagnosis, management, and patient outcomes. In this narrative review, we explore the current and emerging opportunities of utilizing AI in the vision sciences, critically examine the associated challenges, and discuss the ethical implications of integrating AI into clinical practice.

Methods: We searched PubMed/MEDLINE and Google Scholar for English-language articles published from January 1, 2005, to March 31, 2025. Studies on AI applications in ophthalmology and optometry, focusing on diagnostic performance, clinical integration, and ethical considerations, were included, irrespective of study design (clinical trials, observational studies, validation studies, systematic reviews, and meta-analyses). Articles not related to the use of AI in vision care were excluded.

Results: AI has achieved high diagnostic accuracy across different ocular domains. In terms of the cornea and anterior segment, AI models have detected keratoconus with sensitivity and accuracy exceeding 98% and 99.6%, respectively, including in subclinical cases, by analyzing Scheimpflug tomography and corneal biomechanics. For cataract surgery, machine learning-based intraocular lens power calculation formulas, such as the Kane and ZEISS AI formulas, reduce refractive errors, achieving mean absolute errors below 0.30 diopters and performing particularly well in highly myopic eyes. AI-based retinal screening systems, such as the EyeArt and IDx-DR, can autonomously detect diabetic retinopathy with sensitivities above 95%, while deep learning models can predict age-related macular degeneration progression with an area under the receiver operating characteristic curve exceeding 0.90. In glaucoma detection, fundus and optical coherence tomography-based AI models have reached pooled sensitivity and specificity exceeding 90%, although performance varies with disease stage and population diversity. AI has also advanced strabismus detection, amblyopia risk prediction, and myopia progression forecasting by using facial analysis and biometric data. Currently, key challenges in implementing AI in ophthalmology include dataset bias, limited external validation, regulatory hurdles, and ethical issues, such as transparency and equitable access.

Conclusions: AI is rapidly transforming vision sciences by improving diagnostic accuracy, streamlining clinical workflow, and broadening access to quality eye care, particularly in underserved regions. Its integration into ophthalmology and optometry thus holds significant promise for enhancing patient outcomes and optimizing healthcare delivery. However, to harness the transformative potential of AI fully, sustained multidisciplinary collaboration, involving clinicians, data scientists, ethicists, and policymakers, is essential. Rigorous validation processes, transparency in algorithm development, and strong ethical oversight are equally important to mitigate risks such as bias, data misuse, and unequal access. Responsible implementation of AI in the vision sciences is essential to ensure that all populations are served equitably.