A foundation model for generalizable disease detection from retinal images

Abstract Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders 1 . We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data.

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• 1 However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications 2 .
• 2 Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications.
• 3 Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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