  Inability to express the confidence level and detect unseen classes has
limited the clinical implementation of artificial intelligence in the
real-world. We developed a foundation model with uncertainty estimation (FMUE)
to detect 11 retinal conditions on optical coherence tomography (OCT). In the
internal test set, FMUE achieved a higher F1 score of 96.76% than two
state-of-the-art algorithms, RETFound and UIOS, and got further improvement
with thresholding strategy to 98.44%. In the external test sets obtained from
other OCT devices, FMUE achieved an accuracy of 88.75% and 92.73% before and
after thresholding. Our model is superior to two ophthalmologists with a higher
F1 score (95.17% vs. 61.93% &71.72%). Besides, our model correctly predicts
high uncertainty scores for samples with ambiguous features, of
non-target-category diseases, or with low-quality to prompt manual checks and
prevent misdiagnosis. FMUE provides a trustworthy method for automatic retinal
anomalies detection in the real-world clinical open set environment.
