评估ChatGPT-4在发展家庭医学住院考试中的作用
Hanu Chaudhari1, Christopher Meaney2, Kulamakan Kulasegaram2
1University Health Network, Toronto, Ontario, Canada.
PLOS digital health
|December 19, 2025
概括
聊天GPT-4可以为家庭医学实习生生成高质量的医学考试问题,在几个领域的表现与人类专家相美. 这表明大型语言模型 (LLM) 的潜力有助于创建教育内容,节省时间和资源.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 评估和评价的评估和评估.
背景情况:
- 创建高质量的医学检查是资源密集型的,需要大量的时间,成本和培训.
- 像ChatGPT-4这样的高级大型语言模型 (LLM) 的出现为简化教育内容开发提供了机会.
研究的目的:
- 开发一种标准化的方法,用于使用ChatGPT-4生成研究生家庭医学多选择题.
- 将ChatGPT-4生成的医学考试问题与由人类专家创建的质量和有效性进行比较.
主要方法:
- 八位学术家庭医学医生评估了来自四组的多选题 (MCQ):人为生成的,ChatGPT-4克隆的,ChatGPT-4小说的和人为编辑的ChatGPT-4问题.
- 通过使用标准化评分系统,对17个质量领域的问题进行了评分.
- 统计分析使用线性混合效应模型来比较质量得分.
主要成果:
- 聊天GPT-4和人类生成的问题都被评为高质量和能够评估更高阶的思维.
- 人类生成的问题与ChatGPT-4问题相比,更少被认为是人工智能生成的问题.
- 在几个质量领域,ChatGPT-4在人类专家方面表现出非劣势 (10%的差距),但没有表现出优势.
结论:
- 在特定的质量方面,ChatGPT-4能够产生高质量的医学检查问题,其性能与人类专家相美.
- 在帮助创建和评估教育材料方面,LLM显得有前途,有可能在医学教育中提供显著的时间和成本效益.
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