A new class of AI models, called foundation models, has entered healthcare. Foundation models violate several basic principles of the standard machine learning paradigm for assessing reliability, making it necessary to rethink what guarantees are required to establish warranted trust in them.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$32.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Grote, T., Genin, K. & Sullivan, E. Philos. Compass 19, e12974 (2024).
Von Luxburg, U. & Schölkopf, B. In Handbook of the History of Logic (eds Gabbay, D. M. et al.) Vol. 10, Ch. 16 (Elsevier North-Holland, 2011).
Finlayson, S. G. et al. New Engl. J. Med 385, 283–286 (2021).
Garcea, F., Serra, A., Lamberti, F. & Morra, L. Comput. Biol. Med. 152, 106391 (2023).
Moor, M. et al. Nature 616, 259–265 (2023).
Schaeffer, R., Miranda, B., & Koyejo, S. In Advances in Neural Information Processing Systems 36 (NeurIPS, 2023).
Ruffolo, J. A. & Madani, A. Nat. Biotech. 42, 200–202 (2024).
Jiang, L. Y. et al. Nature 619, 357–362 (2023).
Acosta, J. N., Falcone, G. J., Rajpurkar, P. & Topol, E. J. Nat. Med. 28, 1773–1784 (2022).
Bommasani, R. et al. Preprint at https://doi.org/10.48550/arXiv.2108.07258 (2021).
DeGrave, A. J., Janizek, J. D. & Lee, S. I. Nat. Mach. Intell. 3, 610–619 (2021).
Kompa, B., Snoek, J. & Beam, A. L. npj Digit. Med. 4, 4 (2021).
Hager, P. et al. Nat. Med. 30, 2613–2622 (2024).
Wornow, M. et al. npj Digit. Med. 6, 135 (2023).
Singhal, K. et al. Nature 620, 172–180 (2023).
Acknowledgements
T.G., T.F. and P.B. all acknowledge support by the German Science Foundation (BE5601/8-1 and the Excellence Cluster 2064 ‘Machine Learning — New Perspectives for Science’, project number 390727645), and the Carl Zeiss Foundation in the project ‘Certification and Foundations of Safe Machine Learning Systems in Healthcare’. In addition, P.B. also acknowledges support by the Hertie Foundation.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no conflict of interest
Peer review
Peer review information
Nature Machine Intelligence thanks the anonymous reviewer(s) for their contribution to the peer review of this work.
Rights and permissions
About this article
Cite this article
Grote, T., Freiesleben, T. & Berens, P. Foundation models in healthcare require rethinking reliability. Nat Mach Intell 6, 1421–1423 (2024). https://doi.org/10.1038/s42256-024-00924-5
Published:
Issue date:
DOI: https://doi.org/10.1038/s42256-024-00924-5
