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

作者：Kai Jin, Yu Tao, Gui‐Shuang Ying, Zongyuan Ge, Kelvin Zhenghao Li, Yukun Zhou, Danli Shi, Meng Wang, Polat Göktaş, Andrzej Grzybowski · 发表于：Advances in Ophthalmology Practice and Research · 年份：2025 · DOI：10.1016/j.aopr.2025.10.004 · 被引用次数：13 · 研究领域：Retinal Imaging and Analysis、Ocular Oncology and Treatments、Artificial Intelligence in Healthcare and Education

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 in...