Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images.

Journal: The British journal of ophthalmology
Published Date: Sep 20, 2024

Abstract

BACKGROUND: Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserving patient privacy.

Authors

  • Hitoshi Tabuchi
    Department of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
  • Justin Engelmann
    School of Informatics, University of Edinburgh, Edinburgh, UK.
  • Fumiatsu Maeda
    Department of Orthoptics and Visual Sciences, Niigata University of Health and Welfare, Niigata, Niigata, Japan.
  • Ryo Nishikawa
    Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan.
  • Toshihiko Nagasawa
    Department of Ophthalmology, Saneikai Tsukazaki Hospital, 68-1 Aboshi Waku, Himeji City, Hyogo Prefecture, 671-1227, Japan. t.nagasawa@tsukazaki-eye.net.
  • Tomofusa Yamauchi
    Department of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
  • Mao Tanabe
    Department of Ophthalmology, Tsukazaki Hospital, Himeji, Japan.
  • Masahiro Akada
    Department of Ophthalmology, Tsukazaki Hospital, 68-1 Waku, Aboshi-ku, Himeji, Hyogo 671-1227, Japan.
  • Keita Kihara
    Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan.
  • Yasuyuki Nakae
    Department of Ophthalmology, Tsukazaki Hospital, Himeji, Hyogo, Japan.
  • Yoshiaki Kiuchi
    Department of Ophthalmology and Visual Sciences, Graduate School of Biomedical Sciences, Hiroshima University, 1-2-3 Minami, Kasumi, Hioroshima, 734-8553, Japan.
  • Miguel O Bernabeu
    Usher Institute, University of Edinburgh, UK.

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