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. 2020 Nov 18;124(4):686–696. doi: 10.1038/s41416-020-01122-x

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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. 2 — DL pathology can be applied to tumour detection and identification of subtype (basic applications) or to predict clinical features of interest (advanced application). Published studies (indicated by reference number) are classified according to the level of evidence (monocentric (internally approved), multicentric (externally approved) or FDA approved). a Basic image analysis tasks, including tumour detection, grading and subtyping. b Advanced image analysis tasks, including those that exceed pathologists’ routine capacities, such as prediction of mutation, prognosis and response. AI artificial intelligence, NSCLC non-small-cell lung cancer, WSI whole-slide image, ER oestrogen receptor, MSI microsatellite instability, GI gastrointestinal, SPOP speckle-type BTB/POZ protein, BAP1 BRCA-associated protein 1, HNSCC head and neck squamous cell carcinoma, CCA cholangiocarcinoma.