• J Am Coll Radiol · Feb 2018

    Machine Learning in Radiology: Applications Beyond Image Interpretation.

    • Paras Lakhani, Adam B Prater, R Kent Hutson, Kathy P Andriole, Keith J Dreyer, Jose Morey, Luciano M Prevedello, Toshi J Clark, J Raymond Geis, Jason N Itri, and C Matthew Hawkins.
    • Department of Radiology, Thomas Jefferson University Hospital, Sidney Kimmel Jefferson Medical College, Philadelphia, Pennsylvania. Electronic address: paras.lakhani@jefferson.edu.
    • J Am Coll Radiol. 2018 Feb 1; 15 (2): 350-359.

    AbstractMuch attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

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