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- Neil Bhatia, Hari Trivedi, Nabile Safdar, and Marta E Heilbrun.
- Emory University School of Medicine, Atlanta, Georgia.
- J Am Coll Radiol. 2020 Nov 1; 17 (11): 1382-1387.
AbstractThe radiology workflow can be segmented into three large groups: pre-interpretative processes, interpretation, and postinterpretative processes. Each stage of this workflow represents quality improvement opportunities for artificial intelligence and machine learning. Although the focus of recent research has been targeted toward optimization of image interpretation, this article describes significant use cases for artificial intelligence in both the pre-interpretative and postinterpretative aspects of radiology. We provide examples of how current applications of AI for quality improvement purposes across the radiology workflow have been implemented and how further integration of these technologies can significantly improve clinical efficiency, reduce radiologist work burden, and ultimately optimize patient care and outcomes.Copyright © 2020 American College of Radiology. Published by Elsevier Inc. All rights reserved.
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