Retrospectively assessing evaluation and management of artificial-intelligence detected nodules on uninterpreted chest radiographs in the era of radiologists shortage.

Journal: European journal of radiology
PMID: 38042019

Abstract

PURPOSE: High volumes of chest radiographs (CXR) remain uninterpreted due to severe shortage of radiologists. These CXRs may be informally reported by non-radiologist physicians, or not reviewed at all. Artificial intelligence (AI) software can aid lung nodule detection. Our aim was to assess evaluation and management by non-radiologists of uninterpreted CXRs with AI detected nodules, compared to retrospective radiology reports.

Authors

  • Zehavit Kirshenboim
  • Efrat Keren Gilat
    Division of Diagnostic Radiology, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University, Israel. Electronic address: ekgilat@gmail.com.
  • Lawrence Carl
    Division of Diagnostic Radiology, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University, Israel. Electronic address: lbycarl@gmail.com.
  • Elena Bekker
    Division of Diagnostic Radiology, Sheba Medical Center, Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University, Israel. Electronic address: Bekker.Elen@sheba.health.gov.il.
  • Noam Tau
    Joint Department of Medical Imaging, Princess Margaret Cancer Centre, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, 610 University Ave, Ste 3-960, Toronto, ON M5G 2M9, Canada.
  • Maximiliano Klug
    Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel.
  • Eli Konen
  • Edith Michelle Marom

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