A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop

被引：74

作者：

Allen, Bibb, Jr. ^{[1
]}

Seltzer, Steven E. ^{[2
,3
]}

Langlotz, Curtis P. ^{[4
]}

Dreyer, Keith P. ^{[5
]}

Summers, Ronald M. ^{[6
]}

Petrick, Nicholas ^{[7
]}

Marinac-Dabic, Danica ^{[8
]}

Cruz, Marisa ^{[9
]}

Alkasab, Tarik K. ^{[5
]}

Hanisch, Robert J. ^{[10
]}

Nilsen, Wendy J. ^{[11
]}

Burleson, Judy ^{[12
]}

Lyman, Kevin ^{[13
]}

Kandarpa, Krishna ^{[14
]}

机构：

[1] Grandview Med Ctr, Dept Radiol, 390 Grandview Pkwy, Birmingham, AL 35243 USA

[2] Brigham & Womens Hosp, Radiol Dept, 75 Francis St, Boston, MA 02115 USA

[3] Harvard Med Sch, Radiol, Boston, MA 02115 USA

[4] Stanford Univ, Dept Radiol, Palo Alto, CA 94304 USA

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

[6] NIH, Radiol & Imaging Sci, Clin Ctr, Bldg 10, Bethesda, MD 20892 USA

[7] US FDA, Ctr Devices & Radiol Hlth, Silver Spring, MD USA

[8] US FDA, Div Epidemiol, Ctr Devices & Radiol Hlth, Silver Spring, MD USA

[9] US FDA, Digital Hlth Unit, Ctr Devices & Radiol Hlth, Silver Spring, MD USA

[10] NIST, Off Data & Informat, Mat Measurement Lab, Gaithersburg, MD 20899 USA

[11] Natl Sci Fdn, Div Informat & Intelligent Syst, Alexandria, VA USA

[12] Amer Coll Radiol, Dept Qual & Safety, Reston, VA USA

[13] Enlitic, San Francisco, CA USA

[14] Natl Inst Biomed Imaging & Bioengn, Res Sci & Strateg Direct, Off Director, NIH, Bethesda, MD USA

来源：

JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY | 2019年 / 16卷 / 09期

基金：

美国国家卫生研究院;

关键词：

RADIOLOGY;

D O I：

10.1016/j.jacr.2019.04.014

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：

Advances in machine learning in medical imaging are occurring at a rapid pace in research laboratories both at academic institutions and in industry. Important artificial intelligence (AI) tools for diagnostic imaging include algorithms for disease detection and classification, image optimization, radiation reduction, and workflow enhancement. Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower. In August 2018, the National Institutes of Health assembled multiple relevant stakeholders at a public meeting to discuss the current state of knowledge, infrastructure gaps, and challenges to wider implementation. The conclusions of that meeting are summarized in two publications that identify and prioritize initiatives to accelerate foundational and translational research in AI for medical imaging. This publication summarizes key priorities for translational research developed at the workshop including: (1) creating structured AI use cases, defining and highlighting clinical challenges potentially solvable by AI; (2) establishing methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and mitigate unintended bias; (3) establishing tools for validation and performance monitoring of AI algorithms to facilitate regulatory approval; and (4) developing standards and common data elements for seamless integration of AI tools into existing clinical workflows. An important goal of the resulting road map is to grow an ecosystem, facilitated by professional societies, industry, and government agencies, that will allow robust collaborations between practicing clinicians and AI researchers to advance foundational and translational research relevant to medical imaging. (C) 2019 Published by Elsevier on behalf of American College of Radiology

引用

页码：1179 / 1189

页数：11

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