Evidence map›Paper›PMID 38746668›Full record

ArticleiScience2024

The application of large language models in medicine: A scoping review.

Xiangbin Meng, Xiangyu Yan, Kuo Zhang, Da Liu, Xiaojuan Cui, Yaodong Yang, Muhan Zhang, Chunxia Cao, Jingjia Wang, Xuliang Wang and 15 more

Abstract readScoping Review
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 193 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
193citing papers in PubMed, 8 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

193 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Large language models in obesity: a systematic review.International journal of obesity (2005) · 2026
    Pooled it
  4. Pooled it
  5. AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025
    Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Trial
  10. Trial
  11. Trial
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Are large language models such as ChatGPT, capable of supporting patients and general practitioners after spine surgery?European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  18. Article
  19. Review
  20. Article

133 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Xiangbin MengDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Xiangyu YanInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
Kuo ZhangDepartment of Cardiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China.
Da LiuDepartment of Cardiology, the First Hospital of Hebei Medical University, Graduate School of Hebei Medical University, Shi-jia-zhuang, Hebei, China.
Xiaojuan CuiSchool of Software & Microelectronics, Peking University, Beijing, China.
Yaodong YangInstitute for Artificial Intelligence, Peking University, Beijing, China.
Muhan ZhangInstitute for Artificial Intelligence, Peking University, Beijing, China.
Chunxia CaoInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
Jingjia WangDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Xuliang WangDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Jun GaoDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Yuan-Geng-Shuo WangDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Jia-Ming JiInstitute for Artificial Intelligence, Peking University, Beijing, China.
Zifeng QiuPeking University Health Science Center, Peking University First Hospital, Beijing, China.
Muzi LiPeking University Health Science Center, Peking University People's Hospital, Beijing, China.
Cheng QianDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Tianze GuoPeking University Health Science Center, Beijing, China.
Shuangquan MaSchool of Biological Science and Medical Engineering, Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing, China.
Zeying WangDepartment of Prosthodontics, Peking University School and Hospital of Stomatology, National Engineering Laboratory for Digital and Material Technology of Stomatology, National Clinical Research Center for Oral Diseases, Beijing Key Laboratory of Digital Stomatology, Beijing, China.
Zexuan GuoPeking University Health Science Center, Beijing, China.
Youlan LeiPeking University Health Science Center, Beijing, China.
Chunli ShaoDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Wenyao WangDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.
Haojun FanInstitute of Disaster and Emergency Medicine, Tianjin University, Tianjin, China.
Yi-Da TangDepartment of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial intelligenceHealth informatics

Identifiers

PMID38746668
PMCPMC11091685

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.