Turner, Oliver, Saravanan, Chandrassegar, Sing, Tobias, Aeffner, Famke, Bangari, Dinesh, High, Wanda, Knight, Brian, Forest, Tom, Schumacher, Vanessa, Himmel, Lauren, Rudmann, Dan, Bhupinder, Bawa, Muthuswamy, Anantharaman, Edmondson, Elijah, Aina, Olulanu, Cossic, Brieuc, Brown, Danielle and Sebastian, Manu (2019) Society of Toxicologic Pathology Digital Pathology and Image Analysis Special Interest Group Article*: Opinion on the Application of Artificial Intelligence and Machine Learning to Digital Toxicologic Pathology. Toxicologic Pathology. ISSN 15331601

Abstract

Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues.*This article is a product of a Special Interest Group of the Society of Toxicologic Pathology (STP). The views expressed in this article are those of the authors and do not necessarily represent the policies, positions, or opinions of the STP.

Item Type:	Article
Keywords:	artificial intelligence deep learning digital toxicologic pathology machine learning neural networks
Date Deposited:	06 Feb 2020 00:45
Last Modified:	06 Feb 2020 00:45
URI:	https://oak.novartis.com/id/eprint/39686