Deep Learning and Large Models in Ophthalmic Innovations

Dear Colleagues,

Recent years have witnessed remarkable progress in ophthalmic imaging and artificial intelligence, transforming our ability to detect, characterize, and predict a wide range of ocular and systemic diseases. Advanced deep learning models—together with the rapid emergence of large foundation models—have demonstrated unprecedented capabilities in automated image interpretation, multimodal data integration, clinical decision support, and the discovery of novel imaging biomarkers. These innovations are reshaping both research and clinical practice in ophthalmology, extending from retinal and anterior-segment imaging to disease monitoring, risk stratification, and precision treatment planning. 

I would like to invite outstanding researchers to contribute their high-quality work to this Special Issue. Submissions may include, but are not limited to, the following: studies involving ophthalmic image analysis; deep learning or transformer-based architectures; large vision or vision–language models; multimodal learning; generative AI; explainable and trustworthy AI; and clinically oriented applications in screening, diagnosis, and prognostic prediction. We believe this broad collection of research will highlight the transformative potential of modern AI technologies in ophthalmology and serve as a foundation for the next generation of intelligent, clinically actionable ophthalmic innovations.

Prof. Dr. Yanwu Xu
Dr. Weihua Yang
Dr. Huihui Fang
Guest Editors

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