Current applications and future impact of machine learning in radiology

Garry Choy, Omid Khalilzadeh, Mark Michalski, Synho Do, Anthony E. Samir, Oleg S. Pianykh, J. Raymond Geis, Pari V. Pandharipande, James A. Brink, Keith J. Dreyer

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)318-328
Number of pages11
JournalRADIOLOGY
Volume288
Issue number2
DOIs
StatePublished - Aug 2018
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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