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.

  • Patents
  • Published:

Patents

Mapping the patent landscape of medical machine learning

Nature Biotechnology volume 41, pages 461–468 (2023)Cite this article

Subjects

Patent office data show robust and rising patenting of AI inventions in the medical field, contrary to fears that medical machine learning patents might be largely unavailable because of challenges to their subject-matter eligibility.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Annual patenting activity for medical machine learning technologies and legal status.
Fig. 2: Organizations with the highest count of MML pending patent applications, patent grants and expired patents (S5).
Fig. 3: Choice of patent office (USPTO versus EPO) for MML granted patents (S5).
Fig. 4: Granted patents with MML claims per year.
Fig. 5: Analysis of MML claim limitations.
Fig. 6: Classification of granted MML patents (S7) by type of medical application and input signal dimensions.

References

  1. US Food & Drug Administration. Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices (5 October 2022); https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices

  2. Price, N. Cardozo Law Rev. 37, 1401–1453 (2017).

    Google Scholar 

  3. Tull, S. & Miller, P. J. Rob. Artif. Intell. Law 1, 315–325 (2018).

    Google Scholar 

  4. Aboy, M. et al. Nat. Biotechnol. 37, 1118–1125 (2019).

    Article  CAS  PubMed  Google Scholar 

  5. Williams, H. Annu. Rev. Econ. 9, 441–469 (2017).

    Article  Google Scholar 

  6. Bubela, T. et al. Nat. Biotechnol. 31, 202–206 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Smith, J. A. et al. Nat. Biotechnol. 36, 1043–1047 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Aboy, M., Minssen, T. & Kop, M. Rev. Intellect. Prop. Compet. Law 53, 853–882 (2022).

    Google Scholar 

  9. Aboy, M., Liddell, K., Liddicoat, J. & Crespo, C. Nat. Biotechnol. 34, 1119–1123 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Aboy, M., Liddicoat, J., Liddell, K., Jordan, M. & Crespo, C. Nat. Biotechnol. 35, 820–825 (2017).

    Article  CAS  PubMed  Google Scholar 

  11. Aboy, M., Liddell, K., Jordan, M., Crespo, C. & Liddicoat, J. Nat. Biotechnol. 40, 465–471 (2022).

    Article  CAS  PubMed  Google Scholar 

  12. Aboy, M., Crespo, C., Liddell, K., Minssen, T. & Liddicoat, J. Nat. Biotechnol. 37, 513–518 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Reuter, E. 5 takeaways from the FDA’s list of AI-enabled medical devices. MedTech Dive https://www.medtechdive.com/news/FDA-AI-ML-medical-devices-5-takeaways/635908/ (7 November 2022).

  14. Minssen, T., Gerke, S., Aboy, M., Price, N. & Cohen, G. J. Law Biosci. 7, lsaa002 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  15. US Food & Drug Administration. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff (28 September 2022); https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software

Download references

Acknowledgements

The research was supported, in part, by a Novo Nordisk Foundation Grant for a scientifically independent International Collaborative Biomedical Innovation & Law Program - Inter-CeBIL (grant no. NNF17SA027784, Cantab. grant no. G122163). We would like to acknowledge Liz Aboy for her valuable contribution and technical support generating figures for this article.

Author information

Authors and Affiliations

  1. Centre for Law, Medicine, and Life Sciences (LML), Faculty of Law, University of Cambridge, Cambridge, UK

    Mateo Aboy

  2. University of Michigan Law School, Ann Arbor, MI, USA

    W. Nicholson Price II & Seth Raker

Authors
  1. Mateo Aboy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W. Nicholson Price II
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seth Raker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mateo Aboy.

Ethics declarations

Competing interests

The authors declare no competing interests.

Supplementary information

Supplementary Information

Supplementary Data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aboy, M., Price, W.N. & Raker, S. Mapping the patent landscape of medical machine learning. Nat Biotechnol 41, 461–468 (2023). https://doi.org/10.1038/s41587-023-01735-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-023-01735-6

Search

Advanced 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