• Neena Kapoor, Ronilda Lacson, and Ramin Khorasani.
• Director of Diversity, Inclusion, and Equity, Department of Radiology, Brigham and Women's Hospital; Quality and Patient Safety Officer, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. Electronic address: nkapoor@partners.org.
• J Am Coll Radiol. 2020 Nov 1; 17 (11): 1363-1370.

AbstractIn the past decade, there has been tremendous interest in applying artificial intelligence (AI) to improve the field of radiology. Currently, numerous AI applications are in development, with potential benefits spanning all steps of the imaging chain from test ordering to report communication. AI has been proposed as a means to optimize patient scheduling, improve worklist management, enhance image acquisition, and help radiologists interpret diagnostic studies. Although the potential for AI in radiology appears almost endless, the field is still in the early stages, with many uses still theoretical, in development, or limited to single institutions. Moreover, although the current use of AI in radiology has emphasized its clinical applications, some of which are in the distant future, it is increasingly clear that AI algorithms could also be used in the more immediate future for a variety of noninterpretive and quality improvement uses. Such uses include the integration of AI into electronic health record systems to reduce unwarranted variation in radiologists' follow-up recommendations and to improve other dimensions of radiology report quality. In the end, the potential of AI in radiology must be balanced with acknowledgment of its current limitations regarding generalizability and data privacy.Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.

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