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

Front Endocrinol (Lausanne). 2024 Sep 2:15:1466012. doi: 10.3389/fendo.2024.1466012. eCollection 2024.

¹ Department of Radiology, Yongin Severance Hospital, College of Medicine, Yonsei University, Yongin-si, Republic of Korea.
² Department of Radiology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
³ Department of Radiology, CHA University Bundang Medical Center, Seongnam-si, Republic of Korea.
⁴ Department of Surgery, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
⁵ Department of Endocrinology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
⁶ Department of Radiology, Keimyung University Dongsan Hospital, Daegu, Republic of Korea.
⁷ Department of Computational Science and Engineering, Yonsei University, Seoul, Republic of Korea.
⁸ Department of Endocrinology, College of Medicine, Yonsei University, Seoul, Republic of Korea.
⁹ Department of Radiology, College of Medicine, Yonsei University, Seoul, Republic of Korea.

Abstract

[This corrects the article DOI: 10.3389/fendo.2024.1372397.].

Keywords: artificial intelligence; digital learning; learning; thyroid cancer; ultrasound.