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. 2023 Mar 30;6:54. doi: 10.1038/s41746-023-00766-2

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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. 6 — Whereas an underskilling bias is reflected by a discrepancy in the negative predictive value of AI-based predictions across sub-cohorts of surgeons (e.g., s₁ = novice and s₂ = expert), and overskilling bias is reflected by a discrepancy in the positive predictive value.