Peer-Reviewed Publication
J Am Coll Radiol2019;16(9 Pt A):1179-1189.September 1, 2019Consensus Development Conference, NIH
A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop.
Bibb Allen1, Steven E Seltzer2, Curtis P Langlotz3, Keith P Dreyer4, Ronald M Summers5, Nicholas Petrick6, Danica Marinac-Dabic7, Marisa Cruz8, Tarik K Alkasab4, Robert J Hanisch9, Wendy J Nilsen10, Judy Burleson11, Kevin Lyman12, Krishna Kandarpa13
1Department of Radiology, Grandview Medical Center, Birmingham, Alabama. Electronic address: bibb@mac.com.
2Radiology Department, Brigham and Women's Hospital, Boston, Massachusetts; Radiology, Harvard Medical School, Boston, Massachusetts.
3Department of Radiology, Stanford University, Palo Alto, California.
4Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
5Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, Maryland.
6Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland.
7Division of Epidemiology, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland.
8Digital Health Unit, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland.
9Office of Data and Informatics, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland.
10National Science Foundation, Division of Information and Intelligent Systems, Alexandria, Virginia.
11American College of Radiology, Department of Quality and Safety, Reston, Virginia.
12Enlitic, San Francisco, California.
13Research Sciences & Strategic Directions, Office of the Director, National Institute of Biomedical Imaging and Bioengineering, The National Institutes of Health, Bethesda, Maryland.
Abstract
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 occurri…
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