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

Journal: Journal of the American College of Radiology : JACR
Published Date: May 28, 2019

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 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.

Authors

  • Bibb Allen
    Department of Radiology, Grandview Medical Center, Birmingham, Alabama. Electronic address: bibb@mac.com.
  • Steven E Seltzer
    Radiology Department, Brigham and Women's Hospital, Boston, Massachusetts; Radiology, Harvard Medical School, Boston, Massachusetts.
  • Curtis P Langlotz
    Stanford University, University Medical Line, Stanford, CA, 94305, US.
  • Keith P Dreyer
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.
  • Nicholas Petrick
  • Danica Marinac-Dabic
    Office for Clinical Evidence and Analysis, United States Food and Drug Administration, Silver Spring, MD, United States.
  • Marisa Cruz
    Digital Health Unit, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland.
  • Tarik K Alkasab
    Massachusetts General Hospital and Harvard Medical School, Radiology, 25 New Chardon Street, Suite 400B, Boston, MA, 02114, USA.
  • Robert J Hanisch
    Office of Data and Informatics, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland.
  • Wendy J Nilsen
    National Science Foundation, Division of Information and Intelligent Systems, Alexandria, Virginia.
  • Judy Burleson
    American College of Radiology, Department of Quality and Safety, Reston, Virginia.
  • Kevin Lyman
    Enlitic, San Francisco, California.
  • Krishna Kandarpa
    From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.).

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