Turner, Oliver, Rudmann, Daniel, Knight, Brian, Zuraw, Aleksandra and Litjens, Geert (2021) Mini Review: The Last Mile—Opportunities and Challenges for Machine Learning in Digital Toxicologic Pathology. Toxicologic pathology.

Abstract

The 2019 manuscript by the Special Interest Group on Digital Pathology and Image Analysis of the Society of Toxicologic pathology suggested that a synergism between artificial intelligence (AI) and machine learning (ML) technologies and digital toxicologic pathology would improve the daily workflow and future impact of toxicologic pathologists globally. Now 2 years later, the authors of this review consider whether, in their opinion, there is any evidence that supports that thesis. Specifically, we consider the opportunities and challenges for applying ML (the study of computer algorithms that are able to learn from example data and extrapolate the learned information to unseen data) algorithms in toxicologic pathology and how regulatory bodies are navigating this rapidly evolving field. Although we see similarities with the “Last Mile” metaphor, the weight of evidence suggests that toxicologic pathologists should approach ML with an equal dose of skepticism and enthusiasm. There are increasing opportunities for impact in our field that leave the authors cautiously excited and optimistic. Toxicologic pathologists have the opportunity to critically evaluate ML applications with a “call-to-arms” mentality. Why should we be late adopters? There is ample evidence to encourage engagement, growth, and leadership in this field.

Item Type:	Article
Keywords:	Artificial intelligence, machine learning, deep learning, neural networks, digital toxicologic pathology
Date Deposited:	04 Mar 2021 00:45
Last Modified:	04 Mar 2021 00:45
URI:	https://oak.novartis.com/id/eprint/43186