Effect of deep learning-based assistive technology use on chest radiograph interpretation by emergency department physicians: a prospective interventional simulation-based study.

Journal: BMC medical informatics and decision making
Published Date: Nov 8, 2021

Abstract

BACKGROUND: Interpretation of chest radiographs (CRs) by emergency department (ED) physicians is inferior to that by radiologists. Recent studies have investigated the effect of deep learning-based assistive technology on CR interpretation (DLCR), although its relevance to ED physicians remains unclear. This study aimed to investigate whether DLCR supports CR interpretation and the clinical decision-making of ED physicians.

Authors

  • Ji Hoon Kim
    Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
  • Sang Gil Han
    Department of Emergency Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
  • Ara Cho
    Biosafety Research Institute and Laboratory of Pathology, College of Veterinary Medicine, Chonbuk National University, Iksan-si,Jeollabuk-do 54596, Republic of Korea.
  • Hye Jung Shin
    Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Song-Ee Baek
    Department of Radiology, Division of Emergency Radiology, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. SONGEEBAEK@yuhs.ac.

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