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大型语言模型作为生成放射学委员会风格多选题的工具

Neel P Mistry¹, Huzaifa Saeed², Sidra Rafique³

¹College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada (N.P.M., H.S., H.O., S.J.A.); Department of Medical Imaging, Royal University Hospital, Saskatoon, Saskatchewan, Canada (N.P.M., H.O., S.J.A.).

Academic radiology

|July 16, 2024

概括

大型语言模型 (LLM) 显示了创建放射学委员会式多选题 (MCQ) 的潜力. GPT-4的高准确性和质量与现有考试材料相提并论,这表明它对教育工作者有用.

科学领域:

医疗教育中的人工智能
放射学教育技术技术

背景情况:

放射学委员会风格的多选题 (MCQ) 对于居民的评估和学习至关重要.
开发高质量的MCQ需要教育工作者大量的时间和专业知识.
大型语言模型 (LLM) 为高效的MCQ生成提供了一个潜在的解决方案.

研究的目的:

评估LLM在生成放射学委员会式MCQ,答案和理由方面的能力.
将LLM产生的MCQ质量与现有的董事会考试项目进行比较.

主要方法:

两个LLM,Llama 2和GPT-4,被用来创建104个MCQ基于美国放射学委员会考试蓝图.
经过董事会认证的放射科医生评估了LLM生成的MCQ和美国放射学学院 (ACR) 诊断放射学培训 (DXIT) 考试项目中的清晰度,相关性,难度,分心因素和合理性充分性.
在评估过程中,放射科医生无法识别MCQ的来源.

主要成果:

在所有评估标准中,GPT-4生成的MCQ获得了与ACR DXIT项目相似的得分,评级接近完美.
拉玛2生成的MCQ得分低于ACR DXIT项目,但仍然被认为是合适的.
在答案生成方面,GPT-4实现了100%的准确性,而Llama 2则实现了69%的准确性.

结论:

像GPT-4这样的高级LLM可以有效地生成高质量的放射学委员会式MCQ和理由.
LLM可以成为放射学教育工作者增强考试准备材料和扩大问题库的宝贵工具.
使用LLM可以促进MCQ作为放射学教学和学习工具的更广泛应用.

关键词:

人工智能的人工智能是人工智能.评估评估的方法大型语言模型.医学教育医学教育多选题的问题.

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