Current Applications and Future Impact of Machine Learning in Radiology.

By M. M. •. Garry Choy, MD Mph Omid Khalilzadeh, MD • Mark Michalski, PhD • Synho Do, M. M. •. Anthony E. Samir, PhD Oleg S. Pianykh, MD • • J. Raymond Geis, M. M. •. Pari V. Pandharipande, MD • James A. Brink, D. P. Keith J. Dreyer. 2018

704 citations recorded by the source corpus.

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

• Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers (2019, background): “Notably, there is a remarkable interest in using AI for diagnostic analysis of various types of medical images, primarily through convolutional neural networks, a type of deep learning technology referred to as “computer vision” (2-4).”