Review ArticlesExplainable AI (xAI) for Anatomic PathologyTosun, Akif B. PhD*; Pullara, Filippo PhD*; Becich, Michael J. MD, PhD*,†; Taylor, D. Lansing PhD*,‡,§; Fine, Jeffrey L. MD*,∥,¶; Chennubhotla, S. Chakra PhD*,§ Author Information *SpIntellx Inc. Departments of †Biomedical Informatics §Computational and Systems Biology ∥Pathology, University of Pittsburgh School of Medicine ‡Drug Discovery Institute, University of Pittsburgh ¶UPMC Magee-Womens Hospital, Pittsburgh, PA This study was partially supported by NSF SBIR Phase I Award #1843825. J.L.F., M.J.B., D.L.T., and S.C.C. are co-founders with equity in SpIntellx Inc. The remaining authors have no funding or conflicts of interest to disclose. All figures can be viewed online in color at www.anatomicpathology.com. Reprints: S. Chakra Chennubhotla, PhD, SpIntellx Inc., 2425 Sidney St., Pittsburgh, PA 15203 (e-mail: [email protected]). Advances In Anatomic Pathology: July 2020 - Volume 27 - Issue 4 - p 241-250 doi: 10.1097/PAP.0000000000000264 Buy Metrics Abstract Pathologists are adopting whole slide images (WSIs) for diagnosis, thanks to recent FDA approval of WSI systems as class II medical devices. In response to new market forces and recent technology advances outside of pathology, a new field of computational pathology has emerged that applies artificial intelligence (AI) and machine learning algorithms to WSIs. Computational pathology has great potential for augmenting pathologists’ accuracy and efficiency, but there are important concerns regarding trust of AI due to the opaque, black-box nature of most AI algorithms. In addition, there is a lack of consensus on how pathologists should incorporate computational pathology systems into their workflow. To address these concerns, building computational pathology systems with explainable AI (xAI) mechanisms is a powerful and transparent alternative to black-box AI models. xAI can reveal underlying causes for its decisions; this is intended to promote safety and reliability of AI for critical tasks such as pathology diagnosis. This article outlines xAI enabled applications in anatomic pathology workflow that improves efficiency and accuracy of the practice. In addition, we describe HistoMapr-Breast, an initial xAI enabled software application for breast core biopsies. HistoMapr-Breast automatically previews breast core WSIs and recognizes the regions of interest to rapidly present the key diagnostic areas in an interactive and explainable manner. We anticipate xAI will ultimately serve pathologists as an interactive computational guide for computer-assisted primary diagnosis. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.