Implementing Machine Learning in Radiology Practice and Research.

  title={Implementing Machine Learning in Radiology Practice and Research.},
  author={Marc D. Kohli and Luciano M. Prevedello and Ross W. Filice and J. Raymond Geis},
  journal={AJR. American journal of roentgenology},
  volume={208 4},
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… CONTINUE READING

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