Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study.

ABSTRACT:

Background

Large 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 and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.

Objective

This study aims to evaluates the accuracy of 7 LLMs including GPT-3.5, GPT-4.0, GPT-4o, Copilot, ERNIE Bot-3.5, SPARK, and Qwen-2.5 on the CNNLE, focusing on their ability to handle domain-specific nursing knowledge and clinical decision-making. We also explore whether combining their outputs using machine lea

SUBMITTER: Zhu S 

PROVIDER: S-EPMC11759905 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature