A systematic review of vision and vision-language foundation models in ophthalmology

Abstract

Background: Vision and vision-language foundation models, a subset of advanced artificial intelligence (AI) frameworks, have shown transformative potential in various medical fields. In ophthalmology, these models, particularly large language models and vision-based models, have demonstrated great potential to improve diagnostic accuracy, enhance treatment planning, and streamline clinical workflows. However, their deployment in ophthalmology has faced several challenges, particularly regarding generalizability and integration into clinical practice. This systematic review aims to summarize the current evidence on the use of vision and vision-language foundation models in ophthalmology, identifying key applications, outcomes, and challenges. Main text: A comprehensive search on PubMed, Web of Science, Scopus, and Google Scholar was conducted to identify studies published between January 2020 and July 2025. Studies were included if they developed or applied foundation models, such as vision-based models and large language models, to clinically relevant ophthalmic applications. A total of 10 studies met the inclusion criteria, covering areas such as retinal diseases, glaucoma, and ocular surface tumor. The primary outcome measures are model performance metrics, integration into clinical workflows, and the clinical utility of the models. Additionally, the review explored the limitations of foundation models, such as the reliance on large datasets, computational resources, and interpretability challenges. The majority of studies demonstrated that foundation models could achieve high diagnostic accuracy, with several reports indicating excellent performance comparable to or exceeding those of experienced clinicians. Foundation models achieved high accuracy rates up to 95% for diagnosing retinal diseases, and similar performances for detecting glaucoma progression. Despite promising results, concerns about algorithmic bias, overfitting, and the need for diverse training data were common. High computational demands, EHR compatibility, and the need for clinician validation also posed challenges. Additionally, model interpretability issues hindered clinician trust and adoption. Conclusions: Vision and vision-language foundation models in ophthalmology show significant potential for advancing diagnostic accuracy and treatment strategies, particularly in retinal diseases, glaucoma, and ocular oncology. However, challenges such as data quality, transparency, and ethical considerations must be addressed. Future research should focus on refining model performance, improving interpretability and generalizability, and exploring strategies for integrating these models into routine clinical practice to maximize their impact in clinical ophthalmology.