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. 2020 May 28;33(11):2169–2185. doi: 10.1038/s41379-020-0540-1

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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. 1 — Pathologists are recruited worldwide (A). If a pathologist consents to having their images used (B), we download those images (C) and manually annotate them (D). Next, we train a Random Forest classifier to predict image characteristics, e.g., disease state (E). This classifier is used to predict disease and search. If a pathologist posts a case to social media and mentions @pathobot (F), our bot will use the post’s text and images to find similar cases on social media and PubMed (G). The bot then posts summaries and notifies pathologists with similar cases (H). Pathologists discuss the results (I), and some also decide to share their cases with us, initiating the cycle again (A). “Procedure overview” in the supplement explains further (Section S5.4).