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The reporting quality of natural language processing studies: systematic review of studies of radiology reports
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Haibe-Kains, B. et al. Transparency and reproducibility in artificial intelligence. Nature https://doi.org/10.1038/s41586-020-2766-y (2020).
McKinney, S. M. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020).
Kim, H.-E. et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digital Health 2, e138–e148 (2020).
Wu, N. et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging 39, 1184–1194 (2019).
Rodriguez-Ruiz, A. et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J. Natl. Cancer Inst. 111, 916–922 (2019).
Lee, R. S. et al. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4, 170177 (2017).
McKinney, S. M. et al. Addendum: International evaluation of an AI system for breast cancer screening. Nature https://doi.org/10.1038/s41586-020-2679-9 (2020).
Price, W. N., II, Gerke, S. & Cohen, I. G. Potential liability for physicians using artificial intelligence. J. Am. Med. Assoc. 322, 1765–1766 (2019).
Abadi, M. et al. Deep learning with differential privacy. In Proc. 2016 ACM SIGSAC Conference Computer Communications Security CCS’16 308–318 (2016).
We thank A. Dai and E. Gabrilovich for comments.
This study was funded by Google LLC. S.M.M., A.K., D.T., C.J.K, Y.L., G.S.C. and S.S. are employees of Google and own stock as part of the standard compensation package. The authors have no other competing interests to disclose.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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McKinney, S.M., Karthikesalingam, A., Tse, D. et al. Reply to: Transparency and reproducibility in artificial intelligence. Nature 586, E17–E18 (2020). https://doi.org/10.1038/s41586-020-2767-x