Marc Kohli1, Luciano M Prevedello2, Ross W Filice3, J Raymond Geis4. 1. 1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave, M-391, San Francisco, CA 94143. 2. 2 Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH. 3. 3 Department of Radiology, MedStar Georgetown University Hospital, Washington, DC. 4. 4 Department of Radiology, University of Colorado School of Medicine, Fort Collins, CO.
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. CONCLUSION: 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.
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. CONCLUSION: 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.
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