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