Large Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis
MedExamLLM is an open-source, freely accessible, and publicly available online platform providing comprehensive performance evaluation information and evidence knowledge about LLMs on medical exams around the world. The MedExamLLM platform serves as a valuable resource for educators, researchers, and developers in the fields of clinical medicine and artificial intelligence. By synthesizing evidence on LLM capabilities, the platform provides valuable insights to support the integration of artificial intelligence into medical education. Limitations include potential biases in the data source and the exclusion of non-English literature. Future research should address these gaps and explore methods to enhance LLM performance in diverse contexts.
Authors
- Erman Wu
- Jiaxue Cha (ORCID: https://orcid.org/0000-0002-5917-5441)
- Yi Zhou (ORCID: https://orcid.org/0000-0002-9912-581X)
- Jiao Wang (ORCID: https://orcid.org/0000-0003-2790-7828)
- Rongrong Wu
- Hui Zong (ORCID: https://orcid.org/0000-0002-9142-5017)
- Bairong Shen (ORCID: https://orcid.org/0000-0003-2899-1531)
- Chi Zhang (ORCID: https://orcid.org/0009-0000-7838-9114)
- Jiakun Li (ORCID: https://orcid.org/0000-0003-4996-9907)
- Wei-Zhe Feng (ORCID: https://orcid.org/0009-0008-8884-0114)
Institutions
- Tongji University (CN)
- Xinjiang Medical University (CN)
- Sichuan University (CN)
- First Affiliated Hospital of Xinjiang Medical University (CN)
- West China Hospital of Sichuan University (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-12-10
- DOI
- https://doi.org/10.2196/66114
- Citations
- 61
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 7.34