Machine Learning in Radiology: Applications Beyond Image Interpretation

被引：151

作者：

Lakhani, Paras ^{[1
]}

Prater, Adam B. ^{[2
]}

Hutson, R. Kent ^{[3
,8
]}

Andriole, Kathy P. ^{[4
]}

Dreyer, Keith J. ^{[5
]}

Morey, Jose ^{[6
,7
,8
]}

Prevedello, Luciano M. ^{[9
]}

Clark, Toshi J. ^{[10
]}

Geis, J. Raymond ^{[10
]}

Itri, Jason N. ^{[7
]}

Hawkins, C. Matthew ^{[2
]}

机构：

[1] Thomas Jefferson Univ Hosp, Sidney Kimmel Jefferson Med Coll, Dept Radiol, Philadelphia, PA 19107 USA

[2] Emory Univ, Sch Med, Dept Radiol & Imaging Sci, Atlanta, GA USA

[3] Radiol Alliance, Colorado Springs, CO USA

[4] Harvard Med Sch, Brigham & Womens Hosp, Dept Radiol, Boston, MA USA

[5] Harvard Med Sch Boston, Massachusetts Gen Hosp, Dept Radiol, Boston, MA USA

[6] IBM Watson Res, Yorktown Hts, NY USA

[7] Univ Virginia, Dept Radiol, Charlottesville, VA USA

[8] Med Ctr Radiologists, Virginia Beach, VA USA

[9] Ohio State Univ, Med Ctr, Dept Radiol, Columbus, OH 43210 USA

[10] Univ Colorado, Med Ctr, Denver, CO 80202 USA

来源：

JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY | 2018年 / 15卷 / 02期

关键词：

Artificial intelligence; machine learning; deep learning; radiology; workflows; NEURAL-NETWORKS; LANGUAGE; PATIENT; CLASSIFICATION; SOCIETY; FUTURE; MODEL;

D O I：

10.1016/j.jacr.2017.09.044

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.

引用

页码：350 / 359

页数：10

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