Evidence map›Paper›PMID 42018550›Full record

ArticlePloS one2026

Evaluation of large language models in medical examinations: A scoping review protocol.

Weiqi Wang, Baifeng Wang, Yan Zhu, Zhe Wang, Suyuan Peng

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Article
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

5 authors.

Weiqi WangCollege of Medical Information, Changchun University of Chinese Medicine, Changchun City, Jilin Province, China.
Baifeng WangDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Yan ZhuInstitute of Information on Traditional Chinese Medicine China Academy of Chinese Medical Sciences, Beijing, China.
Zhe WangInstitute of Basic Medical Sciences and School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Suyuan PengInstitute of Information on Traditional Chinese Medicine China Academy of Chinese Medical Sciences, Beijing, China.ORCID https://orcid.org/0000-0002-8221-7574

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionStandardized medical examinations, used to assess trainee clinical competencies, provide a rigorous means to verify LLM accuracy and reliability in medical contexts. Although current evaluations use these exams to test LLMs' clinical reasoning, significant performance variations occur across different clinical scenarios. Existing methods struggle to adapt to evolving research needs. This study synthesizes prior research on LLMs in medical exams, highlighting current limitations and proposing future research directions. METHODS AND ANALYSIS: The formulation of the protocol was guided by the standards set forth in the JBI Manual for Evidence Synthesis. Following the establishment of precise inclusion/exclusion criteria and search strategies, we will execute systematic searches in the PubMed and Web of Science Core Collection databases. The method encompasses literature review, data extraction, analytical frameworks, and process mapping. By employing this method, researchers maintain methodological rigor during the entire research process. ETHICS AND DISSEMINATION: This protocol describes a method for performing a scoping review. The investigation focuses on the organized synthesis and examination of previously published research. It does not include human/animal experimentation or sensitive data collection. Ethical approval is not required for this literature-based study.

Indexed as

Large Language ModelsHumansScoping Reviews as Topic

Identifiers

PMID42018550
PMCPMC13102214

What OpenQuestion holds

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Registered trials

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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.