[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
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.

  • Comment
  • Published:

Synthetic data in machine learning for medicine and healthcare

Nature Biomedical Engineering volume 5, pages 493–497 (2021)Cite this article

Subjects

The proliferation of synthetic data in artificial intelligence for medicine and healthcare raises concerns about the vulnerabilities of the software and the challenges of current policy.

Access through your institution
Buy or subscribe

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

Relevant articles

Open Access articles citing this article.

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Synthetic medical data in action.

References

  1. Pencina, M. J., Goldstein, B. A. & D’Agostino, R. B. N. Engl. J. Med. 382, 1583–1586 (2020).

    Article  Google Scholar 

  2. Oxley, T. J. et al. N. Engl. J. Med. 382, e60 (2020).

    Article  Google Scholar 

  3. Trister, A. D. JAMA Oncol. 5, 1429–1430 (2019).

    Article  Google Scholar 

  4. Wang, X. et al. IEEE Trans. Intell. Transp. Syst. 19, 910–920 (2017).

    Article  Google Scholar 

  5. Chesney, B. & Citron, D. Calif. Law Rev. 107, 1753 (2019).

    Google Scholar 

  6. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback (FDA, 2019); https://go.nature.com/3zefINL

  7. Benjamens, S., Dhunnoo, P. & Mesko, B. npj Digit. Med. 3, 118 (2020).

    Article  Google Scholar 

  8. Abowd, J. M. & Vilhuber, L. In International Conference on Privacy in Statistical Databases 239–246 (Springer, 2008).

  9. Beaulieu-Jones, B. K. et al. Circ. Cardiovasc. Qual. Outcomes 12, e005122 (2019).

    Article  Google Scholar 

  10. Artificial Intelligence in Healthcare Market Worth $45.2 Billion by 2026 (Markets and Markets, 2020); https://go.nature.com/357P9fA

  11. LeCun, Y., Bengio, Y. & Hinton, G. Nature 521, 436–444 (2015).

    Article  CAS  Google Scholar 

  12. Goodfellow, I. et al. In Advances in Neural Information Processing Systems (eds Ghahramani, Z. et al.) 2672–2680 (MIT Press, 2014).

  13. Ghorbani, A., Natarajan, V., Coz, D. & Liu, Y. In Proceedings of the Machine Learning for Health NeurIPS Workshop (eds Dalca, A. V. et al.) 155–170 (PMLR, 2020).

  14. Mahmood, F. et al. IEEE Trans. Med. Imaging 39, 3257–3267 (2019).

    Article  Google Scholar 

  15. Mahmood, F., Chen, R. & Durr, N. J. IEEE Trans. Med. Imaging 37, 2572–2581 (2018).

    Article  Google Scholar 

  16. Teixeira, B. et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 9059–9067 (IEEE, 2018).

  17. Waheed, A. et al. IEEE Access 8, 91916–91923 (2020).

    Article  Google Scholar 

  18. Tang, Y., Tang, Y., Zhu, Y., Xiao, J. & Summers, R. M. Med. Image Anal. 67, 101839 (2021).

    Article  Google Scholar 

  19. Costa, P. et al. IEEE Trans. Med. Imaging 37, 781–791 (2017).

    Article  Google Scholar 

  20. Frangi, A. F., Tsaftaris, S. A. & Prince, J. L. IEEE Trans. Med. Imaging 37, 673–679 (2018).

    Article  Google Scholar 

  21. Nie, D. et al. IEEE Trans. Biomed. Eng. 65, 2720–2730 (2018).

    Article  Google Scholar 

  22. Zhou, T., Fu, H., Chen, G., Shen, J. & Shao, L. IEEE Trans. Med. Imaging 39, 2772–2781 (2020).

    Article  Google Scholar 

  23. Karras, T., Aila, T., Laine, S. & Lehtinen, J. In International Conference on Learning Representations (OpenReview.net, 2018).

  24. El Emam, K. & Hoptroff, R. Executive Update: The Synthetic Data Paradigm for Using and Sharing Data (Cutter Consortium, 2019); https://go.nature.com/356Pm2E

  25. Chen, D., Yu, N., Zhang, Y. & Fritz, M. In Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security 343–362 (ACM, 2020).

  26. Cheng, V., Suriyakumar, V. M., Dullerud, N., Joshi, S. & Ghassemi, M. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency 149–160 (ACM, 2021).

  27. Xu, C. et al. IEEE Trans. Inform. Foren. Secur. 14, 2358–2371 (2019).

    Article  Google Scholar 

  28. Torkzadehmahani, R., Kairouz, P. & Paten, B. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, 2019).

  29. Chang, Q. et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 13856–13866 (IEEE, 2020).

  30. Yale, A. et al. Neurocomputing 416, 244–255 (2020).

    Article  Google Scholar 

  31. Jordon, J., Yoon, J. & Van Der Schaar, M. In International Conference on Learning Representations (OpenReview.net, 2018).

  32. Movshovitz-Attias, Y., Kanade, T. & Sheikh, Y. In European Conference on Computer Vision 202–217 (Springer, 2016).

  33. Wan, C. & Jones, D. T. Nat. Mach. Intell. 2, 540–550 (2020).

    Article  Google Scholar 

  34. Bolanos, L. A. et al. Nat. Methods 18, 378–381 (2021).

    Article  CAS  Google Scholar 

  35. Padala, S. A. et al. Epidemiology of renal cell carcinoma. World J. Oncol. 11, 79–87 (2020).

    Article  Google Scholar 

  36. Chen, R. et al. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2145–2155 (ACM, 2019).

  37. Shapiro, A. et al. Patterns 2, 100188 (2021).

    Article  Google Scholar 

  38. Salimans, T. et al. In Advances in Neural Information Processing Systems (eds Lee, D. D. et al.) (Curran Associates Inc., 2016).

  39. Zhou, S. et al. In International Conference on Learning Representations (OpenReview.net, 2019).

  40. Choi, E. et al. In Machine Learning for Healthcare 286–305 (PMLR, 2017).

  41. Chen, J., Chun, D., Patel, M., Chiang, E. & James, J. BMC Med. Inform. Decis. 19, 1–9 (2019).

    Article  Google Scholar 

  42. Ive, J. et al. npj Digit. Med. 3, 69 (2020).

    Article  Google Scholar 

  43. Tucker, A., Wang, Z., Rotalinti, Y. & Myles, P. npj Digit. Med. 3, 147 (2020).

    Article  Google Scholar 

  44. Zhang, Z., Yan, C., Lasko, T. A., Sun, J. & Malin, B. A. J. Am. Med. Inform. Assoc. 28, 596–604 (2021).

    Article  Google Scholar 

  45. Stupp, C. Fraudsters used AI to mimic CEO’s voice in unusual cybercrime case. The Wall Street Journal https://go.nature.com/3iqKhKi (30 August 2019).

  46. Finlayson, S. G. et al. Science 363, 1287–1289 (2019).

    Article  CAS  Google Scholar 

  47. Gafni, O., Wolf, L. & Taigman, Y. In Proceedings of the IEEE/CVF International Conference on Computer Vision 9378–9387 (IEEE, 2019).

  48. Zhu, B., Fang, H., Sui, Y. & Li, L. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 414–420 (ACM, 2020).

  49. Wosik, J. et al. J. Am. Med. Inform. Assoc. 27, 957–962 (2020).

    Article  Google Scholar 

  50. Dick, P. K. How To Build A Universe That Doesn’t Fall Apart Two Days Later: The Shifting Realities of Philip K. Dick: Selected Literary and Philosophical Writings 259–280 (Doubleday, 1978).

  51. Tzachor, A. et al. Nat. Mach. Intell. 2, 365–366 (2020).

    Article  Google Scholar 

  52. Jiang, Y., Chen, H., Loew, M. & Ko, H. IEEE J. Biomed. Health 27, 957–962 (2020).

    Google Scholar 

  53. Wang, L., Chen, J. & Marathe, M. ACM Trans. Spat. Algorithms Syst. 6, 1–39 (2020).

    Google Scholar 

  54. Bao, H., Zhou, X., Zhang, Y., Li, Y. & Xie, Y. In Proceedings of the 28th International Conference on Advances in Geographic Information Systems (eds Lu, C.-T. et al.) 273–282 (ACM, 2020).

  55. Bengio, Y. et al. In International Conference on Learning Representations https://go.nature.com/2SgfhlF (2020).

  56. El Emam, K., Mosquera, L., Jonker, E. & Sood, H. J. J. Am. Med. Inform. Assoc. Open 4, ooab012 (2021).

    Google Scholar 

  57. Off road, but not offline: how simulation helps advance our Waymo Driver. Waymo https://go.nature.com/2TXz0XF (28 April 2020).

  58. Wu, J., Yildirim, I., Lim, J. J., Freeman, B. & Tenenbaum, J. In Proceedings of the First 12 Conferences in Advances in Neural Information Processing Systems (eds Jordan, M. I. et al.) 127–135 (2015).

  59. Varol, G. et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 109–117 (IEEE, 2017).

  60. Shrivastava, A. et al. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2107–2116 (IEEE, 2017).

  61. Sankaranarayanan, S., Balaji, Y., Jain, A., Nam Lim, S. & Chellappa, R. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 3752–3761 (IEEE, 2018).

  62. Prakosa, A. et al. IEEE Trans. Med. Imaging 32, 99–109 (2012).

    Article  Google Scholar 

  63. Mahmood, F., Chen, R., Sudarsky, S., Yu, D. & Durr, N. J. Phys. Med. Biol. 63, 185012 (2018).

    Article  Google Scholar 

  64. Incetan, K. et al. Med. Image Anal. 70, 101990 (2021).

    Article  Google Scholar 

  65. Beede, E. et al. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–12 (ACM, 2020).

  66. Johnson-Roberson, M. et al. In Proceedings of the IEEE International Conference on Robotics and Automation 746–753 (IEEE, 2017).

  67. Qiu, W. & Yuille, A. In European Conference on Computer Vision 909–916 (Springer, 2016).

  68. Ramanagopal, M. S., Anderson, C., Vasudevan, R. & Johnson-Roberson, M. IEEE Robot. Autom. Lett. 3, 3860–3867 (2018).

    Article  Google Scholar 

  69. Naeem, M. F., Oh, S. J., Uh, Y., Choi, Y. & Yoo, J. In International Conference on Machine Learning 7176–7185 (PMLR, 2020).

  70. Sajjadi, M. S., Bachem, O., Lucic, M., Bousquet, O. & Gelly, S. Preprint at https://arxiv.org/abs/1806.00035 (2018).

  71. Alaa, A. M., van Breugel, B., Saveliev, E. & van der Schaar, M. In International Conference on Machine Learning (PMLR, 2021); preprint at https://arxiv.org/abs/2102.08921 (2021).

  72. Senior, A. W. et al. Nature 577, 706–710 (2020).

    Article  CAS  Google Scholar 

  73. Ogden, P. J., Kelsic, E. D., Sinai, S. & Church, G. M. Science 366, 1139–1143 (2019).

    Article  CAS  Google Scholar 

  74. Sheridan, C. Novartis, Sarepta join Dyno’s enterprise to boldly go to new gene therapy frontier. BioWorld https://go.nature.com/3zeAugn (11 May 2020).

  75. Zhavoronkov, A. et al. Nat. Biotechnol. 37, 1038–1040 (2019).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported in part by internal funds from BWH Pathology, a Google Cloud Research Grant, the Nvidia GPU Grant Program and NIGMS R35GM138216 (F.M.). R.J.C. was supported by an NSF Graduate Fellowship. The content is solely the responsibility of the authors and does not reflect the official views of the National Science Foundation or the National Institutes of Health.

Author information

Authors and Affiliations

  1. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Richard J. Chen, Ming Y. Lu, Tiffany Y. Chen, Drew F. K. Williamson & Faisal Mahmood

  2. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Richard J. Chen

  3. Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    Richard J. Chen, Ming Y. Lu, Tiffany Y. Chen, Drew F. K. Williamson & Faisal Mahmood

  4. Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA

    Richard J. Chen, Ming Y. Lu & Faisal Mahmood

Authors
  1. Richard J. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Y. Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tiffany Y. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Drew F. K. Williamson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Faisal Mahmood
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Faisal Mahmood.

Ethics declarations

Competing interests

The authors declare no competing interests.

Supplementary information

Supplementary Information

Supplementary Video 1

GAN training for skin-imaging data.

Supplementary Video 2

GAN training for chest-X-ray data.

Supplementary Video 3

GAN training for renal-pathology data.

Supplementary Video 4

Latent-space interpolation for synthetic skin-imaging data.

Supplementary Video 5

Latent-space interpolation for synthetic chest-X-ray data.

Supplementary Video 6

Latent-space interpolation for synthetic renal-pathology data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, R.J., Lu, M.Y., Chen, T.Y. et al. Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng 5, 493–497 (2021). https://doi.org/10.1038/s41551-021-00751-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-021-00751-8

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research