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Current Applications and Future Impact of Machine Learning in Radiology

Authors :
Mark Michalski
Synho Do
Garry Choy
Keith J. Dreyer
Omid Khalilzadeh
Pari V. Pandharipande
J. Raymond Geis
James A. Brink
Oleg S. Pianykh
Anthony E. Samir
Source :
Radiology. 288:318-328
Publication Year :
2018
Publisher :
Radiological Society of North America (RSNA), 2018.

Abstract

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. © RSNA, 2018

Details

ISSN :
15271315 and 00338419
Volume :
288
Database :
OpenAIRE
Journal :
Radiology
Accession number :
edsair.doi.dedup.....eb5f8aa1ff318ca2247422ed3f5509f9