The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Large language model AI chatbots require approval as medical devices
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Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Healthcare represents a uniquely demanding domain for explainable AI (XAI), where incorrect or biased decisions can directly impact patient outcomes and where regulatory frameworks increasingly mandate algorithmic transparency [7].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [7]
In the citing paper
“Thirdly, the integration of such models in operable clinical software systems faces legal and regulatory challenges because these models require approval as medical devices26.”
From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [26]
In the citing paper
“A particularly evolving and debated topic is the regulation of LLMs, and in which contexts and intended uses do they qualify as medical devices (191,192).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [192]
In the citing paper
“This need is accentuated by the lack of professional guidelines or regulatory oversight within this field23.”
From The ethics of ChatGPT in medicine and healthcare: a systematic review on large language models (LLMs) (2024) · cites this paper as [23]
Citations
Cites
Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083
Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine
Lee, P., Bubeck, S., & Petro, J. (2023) · New England Journal of Medicine · doi:10.1056/nejmsr2214184 · Cited by 1,738
A large language model for electronic health records
Yang, X., Chen, A., PourNejatian, N., Shin, H. C., Smith, K. E., Parisien, C., Compas, C., Martin, C., Costa, A. B., Flores, M. G., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00742-2 · Cited by 847
What ChatGPT and generative AI mean for science
Stokel-Walker, C., & Van Noorden, R. (2023) · Nature · doi:10.1038/d41586-023-00340-6 · Cited by 828
Korngiebel, D. M., & Mooney, S. D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00464-x · Cited by 259
Van Riel, N., Auwerx, K., Debbaut, P., Van Hees, S., & Schoenmakers, B. (2017) · BJGP Open · doi:10.3399/bjgpopen17x100833 · Cited by 159
AI chatbots are coming to search engines — can you trust the results?
Stokel-Walker, C. (2023) · Nature · doi:10.1038/d41586-023-00423-4 · Cited by 23
Research works printed but not linked: 6
- Roose, K. New York Times https://go.nature.com/3X7xXku (5 December 2022).
- Wolfram, S. Stephen Wolfram Writings https://go.nature.com/3qP9hBi (14 February 2023).
- Dorfman, S. PM360 https://go.nature.com/3Jfzbo0 (2019).
- US FDA. https://go.nature.com/3NwlVOc (2022).
- Dawes, R. AINews https://go.nature.com/3p9HRoY (28 October 2020).
- Ingram, D. NBC News https://go.nature.com/43ZEGip (14 January 2023).
Cited by
Sorted by citations
The future landscape of large language models in medicine
Clusmann, J., Kolbinger, F. R., Muti, H. S., Carrero, Z. I., Eckardt, J.-N., Laleh, N. G., Löffler, C. M. L., Schwarzkopf, S.-C., Unger, M., Veldhuizen, G. P., et al. (2023) · Communications Medicine · doi:10.1038/s43856-023-00370-1 · Cited by 1,045
The application of large language models in medicine: A scoping review
Meng, X., Yan, X., Zhang, K., Liu, D., Cui, X., Yang, Y., Zhang, M., Cao, C., Wang, J., Wang, X., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109713 · Cited by 414
Haltaufderheide, J., & Ranisch, R. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01157-x · Cited by 396
Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice
Khera, R., Oikonomou, E. K., Nadkarni, G. N., Morley, J., Wiens, J., Butte, A. J., & Topol, E. J. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.05.003 · Cited by 324
Large AI Models in Health Informatics: Applications, Challenges, and the Future
Qiu, J., Li, L., Sun, J., Peng, J., Shi, P., Zhang, R., Dong, Y., Lam, K., Lo, F. P.-W., Xiao, B. J., et al. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3316750 · Cited by 267
Metadata
- Authors
- Gilbert, S., Harvey, H., Melvin, T., Vollebregt, E., & Wicks, P.
- Affiliations
- Else Kröner Fresenius Center for Digital Health (DE)
- Trinity College Dublin (IE)
- Corresponding author
- Gilbert, Stephen
- Venue
- Nature Medicine
- Published
- 2023-06-30
- Volume
- 29
- Issue
- 10
- Pages
- 2396-2398
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4382678522
- Cited by
- 207
- References
- 7
- Citations by year
- 2026: 42 · 2025: 61 · 2024: 82 · 2023: 22
- FWCI
- 8.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (16KISA100K)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Healthcare Regulation
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- large language models, AI chatbots, medical device regulation, regulatory approval, patient care, AI reliability
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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