Artificial Intelligence in Quality Improvement: Reviewing Uses of Artificial Intelligence in Noninterpretative Processes from Clinical Decision Support to Education and Feedback.

Journal: Journal of the American College of Radiology : JACR
Published Date: Nov 1, 2020

Abstract

The 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.

Authors

  • Neil Bhatia
    Emory University School of Medicine, Atlanta, Georgia.
  • Hari Trivedi
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • Nabile Safdar
    Department of Radiology, Medical College of Georgia at Augusta University, 1120 15th St, Augusta, GA 30912 (Y.T.); and Department of Radiology, Emory University, Atlanta, Ga (B.V., E.K., A.P., J.G., N.S., H.T.).
  • Marta E Heilbrun
    Department of Radiology and imaging Sciences, Emory University School of Medicine, Atlanta, Georgia. Electronic address: marta.heilbrun@emory.edu.

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