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. 2024 Jul 4;30(10):2977–2989. doi: 10.1038/s41591-024-03118-z

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© The Author(s) 2024

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 5 — a, The figure presents the Pearson correlation coefficient across different diagnostic categories, comparing assessments from the neurologists (n = 12) and the model, marked as ‘M’. Each diagnostic category from NC to ODE includes a matrix reflecting correlation coefficient values between individual neurologists and the model. Shades of green signify positive correlation, indicating agreement between the model and neurologists, whereas magenta shades suggest negative correlations, indicating potential discrepancies in assessments. The mean pairwise Pearson correlation coefficient for each etiology is presented along with a 95% confidence interval. The symbol ‘X’ denotes rater pairs where the Pearson correlation was not calculable, due to one or both raters giving label-specific confidence scores with no variance. b, The heatmap shows the mean Pearson correlation coefficients between model probabilities and neurologist confidence scores for each label, along with its 95% confidence interval. The correlation coefficient and its confidence interval for each etiology were estimated with a non-parametric bootstrapping approach.