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Authors
Kim, Hee Jeong
Kim, Hak Hee https://orcid.org/0000-0001-9672-844X
Kim, Ki Hwan
Lee, Ji Sung
Choi, Woo Jung
Chae, Eun Young
Shin, Hee Jung
Cha, Joo Hee
Shim, Woo Hyun
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Article History
Received: 8 January 2024
Revised: 22 February 2024
Accepted: 13 March 2024
First Online: 3 April 2024
Declarations
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: The scientific guarantor of this publication is Hak Hee Kim.
: One of the authors (Ki Hwan Kim) is an employee of Lunit, the manufacturer of the AI software used in this study. The other authors declare no competing interests.
: One of the authors, Ji Sung Lee, from the Department of Clinical Epidemiology and Biostatistics at Asan Medical Center, kindly provided statistical advice for this manuscript.
: Written informed consent was waived by the Institutional Review Board.
: Institutional Review Board approval was obtained (Asan Medical Center, approval no. 2019–1566).
: In our current study, 128 out of 2647 women included in the cohort overlap with a previously published study conducted by eight of the authors (Kim, H.J., Kim, H.H., Kim, K.H. et al Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics. Insights Imaging 13, 57 (2022). ). While the previous study focused on the clinical and histopathologic characteristics of mammographically occult breast cancers detected with AI-based software, our current study explored a new application of the same software, by investigating its use for improving the interpretation of breast ultrasound.
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