This article is a correction to:
Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance
Si Eun Lee1
Hye Jung Kim2*Hae Kyoung Jung3Jin Hyang Jung4Jae-Han Jeon5Jin Hee Lee6Hanpyo Hong1Eun Jung Lee7
Daham Kim8
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
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, SwitzerlandCopyright © 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, YW50NjM3QGtudWgua3I=; Jin Young Kwak, ZG9jamluQHl1aHMuYWM=