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. 2023 Oct 24;14:6757. doi: 10.1038/s41467-023-42444-7

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© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 4 — a, b Two samples with correct diagnostic results from both the standard AI model and our UIOS model. c, d Two samples with incorrect diagnostic results from the standard AI model and our UIOS model. Unlike the standard AI model, which directly takes the fundus disease category with the highest probability score as the final diagnosis result, our UIOS will not only give the probability scores but also provide the corresponding uncertainty score to reflect the reliability of the prediction result. If the uncertainty score is less than the threshold theta, indicating the model prediction is reliable; Conversely, if the uncertainty score is greater than the threshold theta, which represents that the result is unreliable, and manual double-checking is required to avoid possible misdiagnosis problems. US uncertainty score, θ threshold theta. Source data are provided as a Source data file.