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. 2024 Jan 11;12:7. doi: 10.1186/s40478-023-01707-6

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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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 7 — An AI-assisted prototype that conceptualizes the augmented pathology. a DFS-quantified mitosis hotspots are calculated based on AI-detected mitoses according to a sensitivity setting. The user can adjust the number of hotspots and adjust the AI sensitivity with the sliders on the right side of the interface. b Once a user selects a DFS-quantified hotspot, the prototype can guide the user to see each HPF inside, where it can mark a box surrounding each AI-detected mitosis candidate with the highest sensitivity. Multiple pathologists may visit the same hotspot areas recommended by the prototype to ensure the highest precision