  Artificial intelligence (AI) is vital in ophthalmology, tackling tasks like
diagnosis, classification, and visual question answering (VQA). However,
existing AI models in this domain often require extensive annotation and are
task-specific, limiting their clinical utility. While recent developments have
brought about foundation models for ophthalmology, they are limited by the need
to train separate weights for each imaging modality, preventing a comprehensive
representation of multi-modal features. This highlights the need for versatile
foundation models capable of handling various tasks and modalities in
ophthalmology. To address this gap, we present EyeFound, a multimodal
foundation model for ophthalmic images. Unlike existing models, EyeFound learns
generalizable representations from unlabeled multimodal retinal images,
enabling efficient model adaptation across multiple applications. Trained on
2.78 million images from 227 hospitals across 11 ophthalmic modalities,
EyeFound facilitates generalist representations and diverse multimodal
downstream tasks, even for detecting challenging rare diseases. It outperforms
previous work RETFound in diagnosing eye diseases, predicting systemic disease
incidents, and zero-shot multimodal VQA. EyeFound provides a generalizable
solution to improve model performance and lessen the annotation burden on
experts, facilitating widespread clinical AI applications for retinal imaging.
