Abstract Fundus diseases are leading causes of global vision impairment, often presenting with complex comorbidities that challenge conventional artificial intelligence models. While ophthalmic foundation models (FMs) offer promising capabilities, their clinical translation for multi-disease detection remains limited by issues such as imbalanced data distribution, uncertainty in multi-label predictions, inter-disease confusion, and domain shifts. Here, we introduce RetExpert, a test-time clinically adaptive framework that enhances FMs for robust and generalizable detection of multiple fundus diseases from color fundus photographs. RetExpert incorporates adaptive knowledge units with a novel stochastic one-hot activation module to improve generalizability, alongside long-tail-aware learning and an uncertainty-aware multi-label learning strategies. It also integrates a fundus disease co-occurrence matrix as medical prior knowledge to mitigate the confusion score ( C -score) between diseases. Furthermore, RetExpert employs a lightweight test-time adaptation method combining unsupervised and pseudo-supervised learning (TTUL + TTPL), enabling dynamic parameter adjustment without full retraining. Extensive evaluations on 15 public and private datasets demonstrate that RetExpert outperforms ophthalmic FMs in detection performance, reliability, and cross-domain adaptability, offering a clinically viable solution for automated multi-disease screening in real-world settings.
Jiang et al. (Mon,) studied this question.
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