A comprehensive overview of medical AI devices approved by the US Food and Drug Administration sheds new light on limitations of the evaluation process that can mask vulnerabilities of devices when they are deployed on patients.
References
Liu, X. et al. Nat. Med. 26, 1364–1374 (2020).
Norgeot, B. et al. Nat. Med. 26, 1320–1324 (2020).
Cruz Rivera, S., Liu, X. & Chan, A. W. et al. Nat. Med. 26, 1351–1363 (2020).
U.S. Food & Drug Administration Center for Devices and Radiological Health. https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm514737.pdf (October 2017).
U.S. Food & Drug Administration Center for Devices and Radiological Health. https://www.fda.gov/media/145022/download (January 2021).
U.S. Food & Drug Administration. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMN/denovo.cfm (accessed 14 December 2020).
U.S. Food & Drug Administration. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfPMA/pma.cfm (accessed 14 December 2020).
U.S. Food & Drug Administration. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm (accessed 14 December 2020).
Benjamens, S., Dhunnoo, P. & Meskó, B. Npj Digit. Med. 3, 1–8 (2020).
American College of Radiology. https://www.acrdsi.org/DSI-Services/FDA-Cleared-AI-Algorithms (accessed 29 November 2020).
Kaushal, A., Altman, R. & Langlotz, C. J. Am. Med. Assoc. 324, 1212–1213 (2020).
Wang, X. et al. 2017 IEEE Conference on Computer Vision and Pattern Recognition 3462–3471 (Institute of Electrical and Electronics Engineers, 2017).
Irvin, J. et al. The Thirty-Third AAAI Conference on Artificial Intelligence 590–597 (Association for the Advancement of Artificial Intelligence, 2019).
Johnson, A. E. W. et al. Sci. Data 6, 317 (2019).
Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K.Q. 2017 IEEE Conference on Computer Vision and Pattern Recognition 4700–4708 (Institute of Electrical and Electronics Engineers, 2017).
Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I. Y. & Ghassemi, M. Pac. Symp. Biocomput. 26, 232–243 (2021).
U.S. Food & Drug Administration. https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/postmarket-requirements-devices (2018).
Ferryman, K. J. Am. Med. Inform. Assoc. 27, 2016–2019 (2020).
Acknowledgements
J.Z. is supported by the National Science Foundation (CCF 1763191 and CAREER 1942926), the US National Institutes of Health (P30AG059307 and U01MH098953) and grants from the Silicon Valley Foundation and the Chan-Zuckerberg Initiative. R.D. is supported by the US National Institutes of Health (T32 5T32AR007422-38). Our compiled database of approved medical AI devices, analysis code, and models used for the case study are all available at https://ericwu09.github.io/medical-ai-evaluation.
Author information
Authors and Affiliations
Contributions
E.W., K.W. and J.Z. designed the study. E.W., K.W. conducted research with help from R.D. and D.O. All the authors contributed to interpretation of the results and writing of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Wu, E., Wu, K., Daneshjou, R. et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat Med 27, 582–584 (2021). https://doi.org/10.1038/s41591-021-01312-x
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41591-021-01312-x
- Springer Nature America, Inc.
This article is cited by
-
Demographic bias in misdiagnosis by computational pathology models
Nature Medicine (2024)
-
Artificial intelligence in the neonatal intensive care unit: the time is now
Journal of Perinatology (2024)
-
Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis
npj Digital Medicine (2024)
-
Generalization—a key challenge for responsible AI in patient-facing clinical applications
npj Digital Medicine (2024)
-
The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation
npj Digital Medicine (2024)