Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA.

Journal: Journal of the American College of Radiology : JACR
PMID: 31319078

Abstract

Artificial intelligence (AI) will reshape radiology over the coming years. The radiology community has a strong history of embracing new technology for positive change, and AI is no exception. As with any new technology, rapid, successful implementation faces several challenges that will require creation and adoption of new integration technology. Use cases important to real-world application of AI are described, including clinical registries, AI research, AI product validation, and computer assistance for radiology reporting. Furthermore, the informatics technologies required for successful implementation of the use cases are described, including open Computer-Assisted Radiologist Decision Support, ACR Assist, ACR Data Science Institute use cases, common data elements (radelement.org), RadLex (radlex.org), LOINC/RSNA RadLex Playbook (loinc.org), and Radiology Report Templates (radreport.org).

Authors

  • Marc Kohli
    1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave, M-391, San Francisco, CA 94143.
  • Tarik Alkasab
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Ken Wang
    Department of Radiology, Baltimore VA Medical Center, Baltimore, Maryland.
  • Marta E Heilbrun
    Department of Radiology and imaging Sciences, Emory University School of Medicine, Atlanta, Georgia. Electronic address: marta.heilbrun@emory.edu.
  • Adam E Flanders
  • Keith Dreyer
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.
  • Charles E Kahn
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.

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