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AJR Am J Roentgenol. 2019 Oct;213(4):782-784. doi: 10.2214/AJR.19.21527. Epub 2019 Jun 5.

Machine Learning for the Interventional Radiologist.

Author information

1
Charles T. Dotter Department of Interventional Radiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239-3011.
2
Department of Radiology, Stanford University School of Medicine, Stanford, CA.

Abstract

OBJECTIVE. The purpose of this article is to describe key potential areas of application of machine learning in interventional radiology. CONCLUSION. Machine learning, although in the early stages of development within the field of interventional radiology, has great potential to influence key areas such as image analysis, clinical predictive modeling, and trainee education. A proactive approach from current interventional radiologists and trainees is needed to shape future directions for machine learning and artificial intelligence.

KEYWORDS:

artificial intelligence; interventional radiology; machine learning

PMID:
31166764
DOI:
10.2214/AJR.19.21527
[Indexed for MEDLINE]

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