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The application of large language models in medicine: A scoping review
Open accessAbstract
This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted in drafting medical documents, creating training simulations, and streamlining research processes. Despite their growing utility in assisted diagnosis and improving doctor-patient communication, challenges persisted, including limitations in contextual understanding and the risk of over-reliance. The surge in LLM-related research indicated a focus on medical writing, diagnostics, and patient communication, but highlighted the need for careful integration, considering validation, ethical concerns, and the balance with traditional medical practice. Future research directions suggested a focus on multimodal LLMs, deeper algorithmic understanding, and ensuring responsible, effective use in healthcare.
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“Large Language Models (LLMs) are increasingly used by healthcare professionals and patients for medical information synthesis.”
From Large Language Model Hallucinations in Spine Surgery: A Comparative Analysis of Clinician vs Patient-Level Prompts (2026) · cites this paper as [1]
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“Existing reviews of LLMs in healthcare have largely neglected a deeper analysis of explainability methods.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [48]
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Pearls and pitfalls of ChatGPT in medical oncology
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Building Emotional Support Chatbots in the Era of LLMsPreprint
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Metadata
- Authors
- Meng, X., Yan, X., Zhang, K., Liu, D., Cui, X., Yang, Y., Zhang, M., Cao, C., Wang, J., Wang, X., Gao, J., Wang, Y.-G.-S., Ji, J.-M., Qiu, Z., Li, M., Qian, C., Guo, T., Ma, S., Wang, Z., Guo, Z., Lei, Y., Shao, C., Wang, W., Fan, H., & Tang, Y.-D.
- Affiliations
- Peking University (CN)
- Tianjin University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Hebei Medical University (CN)
- Peking University Third Hospital (CN)
- Beihang University (CN)
- Corresponding author
- Fan, Haojun
- Tang, Yi-Da
- ORCID
- Venue
- iScience
- Publisher
- Elsevier BV
- Published
- 2024-05
- Volume
- 27
- Issue
- 5
- Pages
- 109713
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC11091685
- OpenAlex
- W4395052272
- Cited by
- 414
- References
- 114
- Citations by year
- 2026: 188 · 2025: 188 · 2024: 38
- FWCI
- 9.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- iScience (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Key Research and Development Program of China Stem Cell and Translational Research (2020YFC2004705)
- National Natural Science Foundation of China (81825003, 82270376, 91957123)
- China Scholarship Council (CSCF2021A04)
- CAMS Innovation Fund for Medical Sciences
- Chinese Academy of Meteorological Sciences (2021-I2M-5-003, T-B-119)
- National Key Research and Development Program of China
- Beijing Municipal Science and Technology Commission, Adminitrative Commission of Zhongguancun Science Park
- Beijing Nova Program (Z201100006820002)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- large language models, ChatGPT, medical applications of AI, medical diagnostics, medical writing, medical education, training simulation, physician-patient communication, overreliance on AI, multimodal large language models, ethics of AI in healthcare
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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