The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
US FDA Approval of Pediatric Artificial Intelligence and Machine Learning–Enabled Medical Devices
Open accessAbstract
This cross-sectional study analyzes the availability of artificial intelligence and machine learning–enabled devices authorized for children by the US Food and Drug Administration (FDA) and assesses reporting of algorithm validation in the pediatric population.
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“Recent work has provided insights into subsets of devices6–11, but a comprehensive characterization of FDA-authorized AI-enabled devices is currently lacking.”
From How AI is used in FDA-authorized medical devices: a taxonomy across 1,016 authorizations (2025) · cites this paper as [11]
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Sammer, M. B. K., Akbari, Y. S., Barth, R. A., Blumer, S. L., Dillman, J. R., Farmakis, S. G., Frush, D. P., Gokli, A., Halabi, S. S., Iyer, R. S., et al. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.06.003 · Cited by 82
Pediatric Medical Device Development and Regulation: Current State, Barriers, and Opportunities
Espinoza, J., Shah, P., Nagendra, G., Bar‐Cohen, Y., & RICHMOND, F. J. R. (2022) · PEDIATRICS · doi:10.1542/peds.2021-053390 · Cited by 64
Advancing Equity in Medical Device Development for Children
Bourgeois, F. T., & Espinoza, J. (2023) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2023.0790 · Cited by 22
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Cited by
How AI is used in FDA-authorized medical devices: a taxonomy across 1,016 authorizations
Singh, R., Bapna, M., Diab, A. R., Ruiz, E. S., & Lotter, W. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01800-1 · Cited by 61
Park, T., Lee, I., Lee, S. W., & Kong, S. W. (2025) · Korean Journal of Pediatrics · doi:10.3345/cep.2025.00962 · Cited by 47
Underrepresentation of children in public medical imaging datasets
Hua, S. B. Z., Heller, N., He, P., Towbin, A. J., Chen, I. Y., Lu, A. X., & Erdman, L. (2026) · Nature Health · doi:10.1038/s44360-026-00111-3 · Cited by 36
Toward governance of artificial intelligence in pediatric healthcare
Richter, F., Holmes, E., Richter, F., Guttmann, K., Duong, S. Q., Gangadharan, S., Schadt, E., Salmasian, H., Gelb, B. D., & Glicksberg, B. S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02000-7 · Cited by 33
Value of artificial intelligence in neuro-oncology
Voigtlaender, S., Nelson, T. A., Karschnia, P., Vaios, E., Kim, M. M., Lohmann, P., Galldiks, N., Filbin, M. G., Azizi, S., Natarajan, V., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100876 · Cited by 27
Metadata
- Authors
- Brewster, R. C. L., Nagy, M., Wunnava, S., & Bourgeois, F. T.
- Affiliations
- Boston Children's Hospital (US)
- Harvard University (US)
- Corresponding author
- Brewster, Ryan C. L.
- Venue
- JAMA Pediatrics
- Publisher
- American Medical Association (AMA)
- Published
- 2025-02-01
- Volume
- 179
- Issue
- 2
- Pages
- 212
- ISSN
- 1072-4710 (ISSN-L)
- 2168-6203
- PMC
- PMC11791695
- OpenAlex
- W4405425835
- Cited by
- 41
- References
- 3
- Citations by year
- 2026: 28 · 2025: 13
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Archives of Pediatrics and Adolescent Medicine (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Regulation
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Safety Systems Engineering in Autonomy
- Keywords
- artificial intelligence, machine learning, medical devices, FDA approval, algorithm validation, children, regulatory oversight
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: US FDA Approval of Pediatric Artificial Intelligence and Machine Learning–Enabled Medical Devices. https://app.cochat.ai/papers/10.1001/jamapediatrics.2024.5437
- The paper
- Brewster, R. C. L., Nagy, M., Wunnava, S., & Bourgeois, F. T. (2025). US FDA Approval of Pediatric Artificial Intelligence and Machine Learning–Enabled Medical Devices. JAMA Pediatrics. https://doi.org/10.1001/jamapediatrics.2024.5437
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
