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The central aim is to improve diagnostic efficiency for rare eye diseases using a generative model.

March 26, 2026npj Digital MedicineOpen Access

Boosting foundation models for rare eye disease diagnosis via a multimodal text-to-image generative framework

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The central aim is to improve diagnostic efficiency for rare eye diseases using a generative model.
Introduced EyeDiff, a generative foundation model for ophthalmic image synthesis.
Utilized textual descriptions to generate lesion-preserving images.
Augmented minority classes across 11 datasets to reduce data imbalance.
Evaluated model performance using objective metrics and expert human evaluations.
EyeDiff produced high-fidelity images representing diverse retinal conditions.
Significant improvement in diagnostic accuracy for rare and common eye diseases.
Enhanced accuracy was observed across different types of foundation models.

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Generative model enhances diagnostic accuracy for retinal diseases in diverse imaging modalities, suggesting improved healthcare outcomes.

Chen et al. (2026) studied this question.

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Generative model enhances diagnostic accuracy for retinal diseases in diverse imaging modalities, suggesting improved healthcare outcomes.