From algorithms to operating room: can large language models master China’s attending anesthesiology exam? A cross-sectional evaluation
LLMs show promise in complex clinical reasoning but risk critical errors in high-risk settings. While useful for education and decision support, their error potential must be carefully assessed in high-stakes environments.
Authors
- Niu Wang (ORCID: https://orcid.org/0009-0003-4050-1870)
- Feng Qin (ORCID: https://orcid.org/0000-0001-9521-7146)
- Zhimin Tan
- Jiuhong Yuan (ORCID: https://orcid.org/0000-0002-0991-1463)
- Qiyu He
- Xian Zhang (ORCID: https://orcid.org/0000-0002-5640-4851)
- Dongxu Chen (ORCID: https://orcid.org/0000-0002-4053-9524)
Institutions
- Sichuan University (CN)
- West China Second University Hospital of Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-09-04
- DOI
- https://doi.org/10.1097/js9.0000000000003406
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65