title: Implementing Machine Learning in Radiology Practice and Research.
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.
  
  
  CONCLUSIONS
  Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement.
authors.affiliation: 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave, M-391, San Francisco, CA 94143.
authors.forename: Marc
authors.initials: M
authors.lastname: Kohli
authors.affiliation: 2 Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH.
authors.forename: Luciano M
authors.initials: LM
authors.lastname: Prevedello
authors.affiliation: 3 Department of Radiology, MedStar Georgetown University Hospital, Washington, DC.
authors.forename: Ross W
authors.initials: RW
authors.lastname: Filice
authors.affiliation: 4 Department of Radiology, University of Colorado School of Medicine, Fort Collins, CO.
authors.forename: J Raymond
authors.initials: JR
authors.lastname: Geis
mesh_terms: D000465:Algorithms; D035843:Biomedical Research / Q000458:organization & administration*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D010818:Practice Patterns, Physicians'; D011871:Radiology / Q000458:organization & administration*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014481:United States
keywords: artificial intelligence; imaging; informatics; machine learning; statistics