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. 2020 Nov 26;11:6004. doi: 10.1038/s41467-020-19817-3

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© The Author(s) 2020

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 Pathologic images of the sole case that was diagnosed as DLBCL by pathologists but was not recognized as DLBCL by our AI model. b Comparison of diagnostic accuracy of DLBCL between pathologists and our AI model by reading additional DLBCL and non-DLBCL lymph node samples (in total 531 400× images) from hospital B. Seven experienced pathologists were invited to read the images. The pathologists used about 60 min on average to read all 531 images with the highest diagnostic accuracy of 74.39%. In contrast, the diagnostic accuracy of our AI model was 100%.