• David B Larson and Giles W Boland.
• Department of Radiology, Stanford University School of Medicine, Stanford, California. Electronic address: david.larson@stanford.edu.
• J Am Coll Radiol. 2019 Sep 1; 16 (9 Pt B): 1259-1266.

AbstractThe advent of artificial intelligence (AI) promises to have a transformational impact on quality in medicine, including in radiology. However, experience has shown that quality tools alone are often not sufficient to bring about consistent excellent performance. Specifically, rather than assuming outcome targets are consistently met, in quality control, managers assume that wide variation is likely present unless proven otherwise with objective performance data. In this article, we discuss what we consider to be the eight essential elements required to achieve comprehensive process control, necessary to deliver consistent quality in radiology: a process control framework, performance measures, performance standards and targets, monitoring applications, prediction models, optimization models, feedback mechanisms, and accountability mechanisms. We consider these elements to be universally applicable, including in the application of AI-based models. We also discuss how the lack of specific elements of a quality control program can hinder widespread quality control efforts. We illustrate the concept using the example of a CT radiation dose optimization and process control program previously developed by one of the authors and provide several examples of how AI-based tools might be used for quality control in radiology.Copyright © 2019. Published by Elsevier Inc.

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