Implementing Machine Learning in Radiology Practice and Research.
AJR Am J Roentgenol. 2017 Apr;208(4):754-760. doi: 10.2214/AJR.16.17224. Epub 2017 Jan 26.
Kohli M1, Prevedello LM2, Filice RW3, Geis JR4.
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.