• Daniel L Rubin.
• Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics Research), Stanford University, Stanford, California. Electronic address: dlrubin@stanford.edu.
• J Am Coll Radiol. 2019 Sep 1; 16 (9 Pt B): 1309-1317.

AbstractRapid technological advancements in artificial intelligence (AI) methods have fueled explosive growth in decision tools being marketed by a rapidly growing number of companies. AI developments are being driven largely by computer scientists, informaticians, engineers, and businesspeople, with much less direct participation by radiologists. Participation by radiologists in AI is largely restricted to educational efforts to familiarize them with the tools and promising results, but techniques to help them decide which AI tools should be used in their practices and to how to quantify their value are not being addressed. This article focuses on the role of radiologists in imaging AI and suggests specific ways they can be engaged by (1) considering the clinical need for AI tools in specific clinical use cases, (2) undertaking formal evaluation of AI tools they are considering adopting in their practices, and (3) maintaining their expertise and guarding against the pitfalls of overreliance on technology.Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.

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