Evaluation of large language models in medical examinations: A scoping review protocol
Clinical Snapshot
PICO Framework
| P — Population | Large language models (LLMs) evaluated in medical examination contexts |
| I — Intervention | Assessment of LLMs using standardized medical examinations |
| C — Comparator | Not applicable (scoping review protocol) |
| O — Outcomes | LLM performance, accuracy, reliability in medical contexts, and identification of research gaps |
Bottom Line
This scoping review protocol aims to synthesize research on large language model performance in medical examinations, addressing a relevant topic in medical education. However, the protocol suffers from significant methodological limitations including an inadequately described search strategy, unclear inclusion criteria, and lack of detailed analytical framework. The restriction to only two databases raises concerns about comprehensiveness. While the research question is timely and relevant to medical education stakeholders, the current protocol requires substantial methodological strengthening before implementation. The findings, when available, may inform medical education bodies about LLM capabilities and limitations in assessment contexts, but the quality of evidence will depend on protocol refinement.
Key Findings
Primary Outcome: Not applicable - protocol paper
Clinical Application
Limited by incomplete methodology description Relevant to Australian medical education bodies (AMC, RACGP) evaluating digital assessment tools, though protocol lacks specificity for local application Medical educators, examination boards, and researchers interested in LLM applications in medical assessment
Abstract
INTRODUCTION: Standardized 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.
References
- 1.Wang, W., Wang, B., Zhu, Y., Wang, Z., & Peng, S. (2026). Evaluation of large language models in medical examinations: A scoping review protocol. PLoS One. https://doi.org/10.1371/journal.pone.0347539
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