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CORRECTION article

Front. Endocrinol., 02 September 2024
Sec. Thyroid Endocrinology

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

Si Eun LeeSi Eun Lee1Hye Jung Kim*Hye Jung Kim2*Hae Kyoung JungHae Kyoung Jung3Jin Hyang JungJin Hyang Jung4Jae-Han JeonJae-Han Jeon5Jin Hee LeeJin Hee Lee6Hanpyo HongHanpyo Hong1Eun Jung LeeEun Jung Lee7Daham KimDaham Kim8Jin Young Kwak*Jin Young Kwak9*
  • 1Department of Radiology, Yongin Severance Hospital, College of Medicine, Yonsei University, Yongin-si, Republic of Korea
  • 2Department of Radiology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
  • 3Department of Radiology, CHA University Bundang Medical Center, Seongnam-si, Republic of Korea
  • 4Department of Surgery, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
  • 5Department of Endocrinology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
  • 6Department of Radiology, Keimyung University Dongsan Hospital, Daegu, Republic of Korea
  • 7Department of Computational Science and Engineering, Yonsei University, Seoul, Republic of Korea
  • 8Department of Endocrinology, College of Medicine, Yonsei University, Seoul, Republic of Korea
  • 9Department of Radiology, College of Medicine, Yonsei University, Seoul, Republic of Korea

A Corrigendum on
Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance

By Lee SE, Kim HJ, Jung HK, Jung JH, Jeon J-H, Lee JH, Hong H, Lee EJ, Kim D and Kwak JY (2024). Front. Endocrinol. 15:1372397. doi: 10.3389/fendo.2024.1372397

In the published article, an author name was incorrectly written as Jing Hyang Jung. The correct spelling is Jin Hyang Jung.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: thyroid cancer, artificial intelligence, ultrasound, learning, digital learning

Citation: Lee SE, Kim HJ, Jung HK, Jung JH, Jeon J-H, Lee JH, Hong H, Lee EJ, Kim D and Kwak JY (2024) Corrigendum: Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance. Front. Endocrinol. 15:1466012. doi: 10.3389/fendo.2024.1466012

Received: 17 July 2024; Accepted: 21 August 2024;
Published: 02 September 2024.

Approved by:

Frontiers Editorial Office, Frontiers Media SA, Switzerland

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) and the copyright owner(s) 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, ant637@knuh.kr; Jin Young Kwak, docjin@yuhs.ac

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.