Application of large language models in medicine
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These frameworks typically focus on narrow clinical scenarios (1-28 datasets), rely on limited data sources or synthetic data, and assess primarily accuracy without comprehensive taxonomy validation or deployment considerations.
“However, these frameworks typically focus on narrow clinical scenarios (1–28 datasets), rely on limited data sources or synthetic data, and assess primarily accuracy without comprehensive taxonomy validation or deployment considerations.”
From Holistic evaluation of large language models for medical tasks with MedHELM (2026) · cites this paper as [26]
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Flagged first, then by citations
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Chow, J. C. L., & Li, K. (2025) · Information · doi:10.3390/info16070549 · Cited by 84 · see citing works
Holistic evaluation of large language models for medical tasks with MedHELM
Bedi, S., Cui, H., Fuentes, M., Unell, A., Wornow, M., Banda, J. M., Kotecha, N., Keyes, T., Mai, Y., Oez, M., et al. (2026) · Nature Medicine · doi:10.1038/s41591-025-04151-2 · Cited by 59 · see citing works
Lin, Y., Luo, Z., Ye, Z., Zhong, N., Zhao, L., Zhang, L., Li, X., Chen, Z., & Chen, Y. (2025) · JMIR Medical Education · doi:10.2196/71125 · Cited by 31 · see citing works
“The primary task involves developing specialized models with medical context adaptive capabilities, specifically enhancing their semantic parsing of unstructured clinical texts to address performance variability in complex case analysis [180,181].”
Vasilev, Y., Raznitsyna, I., Pamova, A., Burtsev, T., Bobrovskaya, T., Kosov, P., Vladzymyrskyy, A., Omelyanskaya, O., & Arzamasov, K. (2025) · Diagnostics · doi:10.3390/diagnostics16010003 · Cited by 5 · see citing works
“They are increasingly integrated into care planning, medical education, and other organizational processes [12].”
- Authors
- Liu, F., Zhou, H., Gu, B., Zou, X., Huang, J., Wu, J., Li, Y., Chen, S. S., Hua, Y., Zhou, P., Liu, J., Mao, C., You, C., Wu, X., Zheng, Y., Clifton, L., Li, Z., Luo, J., & Clifton, D. A.
- Affiliations
- University of Oxford (GB)
- Imperial College London (GB)
- University of Waterloo (CA)
- University of Rochester (US)
- University College London (GB)
- Western University (CA)
- University of Georgia (US)
- Harvard University (US)
- The Hong Kong University of Science and Technology (Guangzhou) (CN)
- Peking University (CN)
- Massachusetts Institute of Technology (US)
- Stony Brook University (US)
- Tencent (China) (CN)
- Amazon (United States) (US)
- Corresponding author
- Liu, Fenglin
- ORCID
- Venue
- Nature Reviews Bioengineering
- Published
- 2025-04-07
- Volume
- 3
- Issue
- 6
- Pages
- 445-464
- ISSN
- 2731-6092 (ISSN-L)
- OpenAlex
- W4409235391
- Cited by
- 94
- References
- 53
- Citations by year
- 2026: 56 · 2025: 38
- FWCI
- 5.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Reviews Bioengineering (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, ChatGPT, medical LLMs, clinical diagnostics, medical education, LLM deployment, model architecture, training data, lightweight models, clinical decision support
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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.
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