Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Reply to: Transparency and reproducibility in artificial intelligence

The Original Article was published on 14 October 2020

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

References

  1. Haibe-Kains, B. et al. Transparency and reproducibility in artificial intelligence. Nature https://doi.org/10.1038/s41586-020-2766-y (2020).

  2. McKinney, S. M. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020).

    Article  CAS  ADS  PubMed  Google Scholar 

  3. 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).

    Article  PubMed  Google Scholar 

  4. Wu, N. et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging 39, 1184–1194 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lee, R. S. et al. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci. Data 4, 170177 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  7. 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).

  8. Price, W. N., II, Gerke, S. & Cohen, I. G. Potential liability for physicians using artificial intelligence. J. Am. Med. Assoc. 322, 1765–1766 (2019).

    Article  Google Scholar 

  9. Abadi, M. et al. Deep learning with differential privacy. In Proc. 2016 ACM SIGSAC Conference Computer Communications Security CCS’16 308–318 (2016).

Download references

Acknowledgements

We thank A. Dai and E. Gabrilovich for comments.

Author information

Authors and Affiliations

Authors

Contributions

This Reply was prepared by a subset of the authors of the original Article in addition to Y.L., all of whom have expertise related to this exchange. S.M.M., A.K., D.T., C.J.K, Y.L., G.S.C. and S.S. wrote and revised this Reply.

Corresponding authors

Correspondence to Scott Mayer McKinney or Shravya Shetty.

Ethics declarations

Competing interests

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-020-2767-x

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing