Evaluating diagnostic content of AI-generated chest radiography: A multi-center visual Turing test.

Journal: PloS one
Published Date: Apr 12, 2023

Abstract

BACKGROUND: Accurate interpretation of chest radiographs requires years of medical training, and many countries face a shortage of medical professionals to meet such requirements. Recent advancements in artificial intelligence (AI) have aided diagnoses; however, their performance is often limited due to data imbalance. The aim of this study was to augment imbalanced medical data using generative adversarial networks (GANs) and evaluate the clinical quality of the generated images via a multi-center visual Turing test.

Authors

  • Youho Myong
    Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.
  • Dan Yoon
    Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, 08826, South Korea.
  • Byeong Soo Kim
    Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea.
  • Young Gyun Kim
    Interdisciplinary Program in Bioengineering, Seoul National University Graduate School, Seoul, Republic of Korea.
  • Yongsik Sim
    From the Department of Radiology, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea (Y.S., K.H., B.W.C.); Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (M.J.C.); Department of Radiology, University Medical Center Freiburg, Freiburg, Germany (E.K.); Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (S. Yune, M.K., S.D.); and Samsung Electronics, Suwon, Republic of Korea (H.K., S. Yang, D.J.L.).
  • Suji Lee
    Ewha Brain Institute, Ewha Womans University, Seoul, South Korea.
  • Jiyoung Yoon
    Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, South Korea.
  • Minwoo Cho
    Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea.
  • Sungwan Kim
    Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Republic of Korea.

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