Implementing Machine Learning in Radiology Practice and Research.

Journal: AJR. American journal of roentgenology
Published Date: Jan 26, 2017

Abstract

OBJECTIVE: The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk.

Authors

  • Marc Kohli
    1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave, M-391, San Francisco, CA 94143.
  • Luciano M Prevedello
  • Ross W Filice
    MedStar Health, MedStar Georgetown University Hospital, 3800 Reservoir Rd, NW CG201, Washington DC, 20007 (R.W.F.); and MedStar Health, National Center for Human Factors in Healthcare, Washington, DC (R.M.R.).
  • J Raymond Geis
    4 Department of Radiology, University of Colorado School of Medicine, Fort Collins, CO.

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