Review ArticlesImplementing Deep Learning Algorithms in Anatomic Pathology Using Open-source Deep Learning LibrariesMcAlpine, Ewen MBBCh, FCPath (SA) (ANAT), MMed*,†; Michelow, Pamela MBBCh, MSc, PGDip (HSE), MIAC*,†Author Information *Division of Anatomical Pathology, School of Pathology, University of the Witwatersrand †Department of Anatomical Pathology, National Health Laboratory Service, Johannesburg, South Africa Supported by the University of the Witwatersrand Research Capex Grant. The authors have no funding or conflicts of interest to disclose. Reprints: Ewen McAlpine, MBBCh, FCPath (SA) (ANAT), MMed, University of the Witwatersrand Medical School, 7 York Road, Parktown, Johannesburg 2193, South Africa (e-mail: firstname.lastname@example.org). Advances In Anatomic Pathology: July 2020 - Volume 27 - Issue 4 - p 260-268 doi: 10.1097/PAP.0000000000000265 Buy Metrics Abstract The application of artificial intelligence technologies to anatomic pathology has the potential to transform the practice of pathology, but, despite this, many pathologists are unfamiliar with how these models are created, trained, and evaluated. In addition, many pathologists may feel that they do not possess the necessary skills to allow them to embark on research into this field. This article aims to act as an introductory tutorial to illustrate how to create, train, and evaluate simple artificial learning models (neural networks) on histopathology data sets in the programming language Python using the popular freely available, open-source libraries Keras, TensorFlow, PyTorch, and Detecto. Furthermore, it aims to introduce pathologists to commonly used terms and concepts used in artificial intelligence. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.