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CORRECTION article

Front. Endocrinol.
Sec. Thyroid Endocrinology
Volume 15 - 2024 | doi: 10.3389/fendo.2024.1466012

Corrigendum: Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital selflearning and artificial intelligence assistance

Provisionally accepted
Si Eun Lee Si Eun Lee 1Hye Jung Kim Hye Jung Kim 2*Hae Kyoung Jung Hae Kyoung Jung 3Jin Hyang Jung Jin Hyang Jung 2Jae-Han Jeon Jae-Han Jeon 2Jin Hee Lee Jin Hee Lee 4Hanpyo Hong Hanpyo Hong 5Eun Jung Lee Eun Jung Lee 6Daham Kim Daham Kim 1JIN YOUNG KWAK JIN YOUNG KWAK 1*
  • 1 College of Medicine, Yonsei University, Seoul, Republic of Korea
  • 2 Kyungpook National University Chilgok Hospital, Daegu, North Gyeongsang, Republic of Korea
  • 3 CHA Bundang Medical Center, Seongnam-si, Republic of Korea
  • 4 Keimyung University Dongsan Hospital, Daegu, North Gyeongsang, Republic of Korea
  • 5 Yongin Severance Hospital, College of Medicine, Yonsei University, Seoul, Seoul, Republic of Korea
  • 6 Yonsei University, Seoul, Seoul, Republic of Korea

The final, formatted version of the article will be published soon.

    Keywords: thyroid cancer, artificial intelligence, ultrasound, Learning, Digital learning

    Received: 17 Jul 2024; Accepted: 21 Aug 2024.

    Copyright: © 2024 Lee, Kim, Jung, Jung, Jeon, Lee, Hong, Lee, Kim and KWAK. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Hye Jung Kim, Kyungpook National University Chilgok Hospital, Daegu, North Gyeongsang, Republic of Korea
    JIN YOUNG KWAK, College of Medicine, Yonsei University, Seoul, Republic of Korea

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