Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study
BackgroundLarge language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain–specific nursing knowledge...
Saved in:
Main Authors: | Shiben Zhu, Wanqin Hu, Zhi Yang, Jiani Yan, Fang Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
JMIR Publications
2025-01-01
|
Series: | JMIR Medical Informatics |
Online Access: | https://medinform.jmir.org/2025/1/e63731 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Large Language Models in Dental Licensing Examinations: Systematic Review and Meta-Analysis
by: Mingxin Liu, et al.
Published: (2025-02-01) -
Performance of Large Language Models on the Korean Dental Licensing Examination: A Comparative Study
by: Woojun Kim, et al.
Published: (2025-02-01) -
Nonacademic predictors of China medical licensing examination
by: Jie Sun, et al.
Published: (2025-01-01) -
Assembly-free reads accurate identification (AFRAID) approach outperforms other methods of DNA barcoding in the walnut family (Juglandaceae)
by: Yanlei Liu, et al.
Published: (2025-01-01) -
EfficientNet-B0 outperforms other CNNs in image-based five-class embryo grading: a comparative analysis
by: Vincent Jaehyun Shim, et al.
Published: (2024-12-01)