Large language models in health care: Development, applications, and challenges
Open accessAbstract
Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre‐training. In the last 3 years, these domain‐specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs as well. This not only emphasizes the significance of developing dedicated LLMs for the specific domains, but also raises expectations for their applications in health care. We believe that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision. Additionally, LLMs hold tremendous promise in assisting with medical education, medical writing and other related applications. Likewise, health care systems must recognize and address the challenges posed by LLMs.
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Current RAG models face challenges related to frequent knowledge updates.
“Furthermore, current RAG models face challenges related to frequent knowledge updates [2,11,21,22].”
From Enhancing Medical Question Answering with LLMs via a Hybrid Retrieval-Augmented Generation Framework (2026) · cites this paper as [2]
The 'Questionable credibility and accuracy of information' section discusses inaccurate LLM outputs, but not retrieval-augmented generation or difficulties with frequent knowledge updates. Searches for RAG, retrieval-augmented generation, and updated or outdated knowledge found no passage addressing the attributed claim; no page number is available in our copy. (LLM, conf=0.75)
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- Authors
- Yang, R., Tan, T. F., Lu, W., Thirunavukarasu, A. J., Ting, D. S. W., & Liu, N.
- Affiliations
- National University of Singapore (SG)
- SingHealth (SG)
- Singapore University of Technology and Design (SG)
- University of Cambridge (GB)
- Corresponding author
- Liu, Nan
- Venue
- Health Care Science
- Publisher
- Wiley
- Published
- 2023-07-24
- Volume
- 2
- Issue
- 4
- Pages
- 255-263
- ISSN
- 2771-1749 (ISSN-L)
- 2771-1757
- PMC
- PMC11080827
- OpenAlex
- W4385227045
- Cited by
- 366
- References
- 48
- Citations by year
- 2026: 102 · 2025: 162 · 2024: 99 · 2023: 3
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Health care science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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, health care applications, ChatGPT, clinical language models, biomedical language models, diagnosis, medical education, medical writing
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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