Large-Scale Study on AI's Impact on Identifying Chest Radiographs with No Actionable Disease in Outpatient Imaging.

Journal: Academic radiology
Published Date: Jul 12, 2024

Abstract

RATIONALE AND OBJECTIVES: Given the high volume of chest radiographs, radiologists frequently encounter heavy workloads. In outpatient imaging, a substantial portion of chest radiographs show no actionable findings. Automatically identifying these cases could improve efficiency by facilitating shorter reading workflows.

Authors

  • Awais Mansoor
    Center for Infectious Disease Imaging (CIDI), Radiology and Imaging Science Department, National Institutes of Health (NIH), Bethesda, MD 20892, United States.
  • Ingo Schmuecking
    Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ.
  • Florin C Ghesu
  • Bogdan Georgescu
  • Sasa Grbic
  • R S Vishwanath
    Siemens Healthineers, Digital Technology and Innovation India, Bengaluru, India.
  • Oladimeji Farri
    Philips Research North America, New York, United States.
  • Rikhiya Ghosh
    Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ.
  • Ramya Vunikili
    Siemens Healthineers, Digital Technology and Innovation, Princeton, NJ.
  • Mathis Zimmermann
    Siemens Healthcare GmbH, Diagnostic Imaging, Erlangen, Germany. Electronic address: mathis.zimmermann@siemens-healthineers.com.
  • James Sutcliffe
    Zwanger-Pesiri Radiology, Lindenhurst, NY.
  • Steven L Mendelsohn
    Zwanger-Pesiri Radiology, Lindenhurst, NY.
  • Dorin Comaniciu
  • Warren B Gefter
    Department of Radiology, University of Pennsylvania Perelman School of Medicine.

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