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- Valeria Makeeva, Judy Gichoya, C Matthew Hawkins, Alexander J Towbin, Marta Heilbrun, and Adam Prater.
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia. Electronic address: valeria.makeeva@emory.edu.
- J Am Coll Radiol. 2019 Sep 1; 16 (9 Pt B): 1254-1258.
AbstractRecent advances in machine learning and artificial intelligence offer promising applications to radiology quality improvement initiatives as they relate to the radiology value network. Coordination within the interlocking web of systems, events, and stakeholders in the radiology value network may be mitigated though standardization, automation, and a focus on workflow efficiency. In this article the authors present applications of these various strategies via use cases for quality improvement projects at different points in the radiology value network. In addition, the authors discuss opportunities for machine-learning applications in data aggregation as opposed to traditional applications in data extraction.Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.
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