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Original Research

Open Access

Effect of an artificial-intelligent chest radiographs reporting system in an emergency department

  • Do Hyeok Yoon1
  • Sejin Heo1,2
  • Jae Yong Yu3
  • Se Uk Lee1
  • Sung Yeon Hwang1
  • Hee Yoon1
  • Tae Gun Shin1
  • Gun Tak Lee1
  • Jong Eun Park1
  • Hansol Chang1,2
  • Taerim Kim1
  • Won Chul Cha1,2,*,

1Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 06351 Seoul, Republic of Korea

2Department of Digital Health, Samsung Advanced Institute for Health Science & Technology (SAIHST), Sungkyunkwan University, 06355 Seoul, Republic of Korea

3Department of Biomedical Systems Informatics, Yonsei University College of Medicine, 03722 Seoul, Republic of Korea

DOI: 10.22514/sv.2023.108 Vol.19,Issue 6,November 2023 pp.144-151

Submitted: 09 March 2023 Accepted: 25 April 2023

Published: 08 November 2023

*Corresponding Author(s): Won Chul Cha E-mail:


Though chest radiography is a first-line diagnostic tool in the emergency department (ED), interpretation has a high error rate. We aimed to evaluate the usability and acceptability of deep learning-based computer-aided detection for chest radiography (DeepCADCR) in an ED environment. We conducted a single-institution survey of emergency physicians (EPs) who had used DeepCADCR (Lunit INSIGHT Chest Xray (CXR), version as part of their ED workflow for at least three months. We developed 22 questions that assessed the subscales of effectiveness, efficiency, safety, satisfaction, and reliability. A seven-point Likert agreement scale was used to rate the responses. A total of 23 EPs who completed the survey was enrolled in the study. When averaged by subscale, satisfaction scores were highest (mean 4.71, standard deviation (SD) 1.43), and safety scores were lowest (mean 4.3, SD 0.72). When scores were converted to acceptability, the total average acceptance of DeepCADCR was 86.0%, with higher scores in ED residents than ED specialists for all subscales. Use of DeepCADCR in the ED workflow was well accepted by EPs.


Artificial intelligence; Deep learning; Chest radiography; Emergency department; Survey; Computer-aided detection

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Do Hyeok Yoon,Sejin Heo,Jae Yong Yu,Se Uk Lee,Sung Yeon Hwang,Hee Yoon,Tae Gun Shin,Gun Tak Lee,Jong Eun Park,Hansol Chang,Taerim Kim,Won Chul Cha. Effect of an artificial-intelligent chest radiographs reporting system in an emergency department. Signa Vitae. 2023. 19(6);144-151.


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