大型语言模型 临床图片和多选择题 对于研究生医学教育
概括
人工智能 (AI) 可以为医学教育生成基于临床图片的多选择题 (MCQ). 专家发现很难区分人工智能生成的MCQ和人类写的MCQ,显示AI.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
背景情况:
- 为医疗培训制定基于临床图片的多选择题 (MCQ) 是耗时的.
- 大型语言模型 (LLM) 为加快MCQ创建提供了一个潜在的解决方案.
研究的目的:
- 评估使用ChatGPT-4进行快速工程技术的有效性,以生成产科妇科MCQ.
- 评估医疗居民和主治医生是否能够区分人类和人工智能生成的MCQ.
主要方法:
- 利用结构化提示工程和代提示链接与权威来源来生成AI MCQ.
- 开发了50个人类和50个人工智能生成的MCQ,随机编译成小题.
- 调查了33名妇产科住院医生和临床医生,以评估MCQ的作者身份和表现.
主要成果:
- 参与者无法可靠地区分人类和人工智能生成的MCQ (正确识别的中位数为39.1%).
- 在正确答案率 (62.3%的人与64.4%人工智能),难度或问题类型之间的歧视指数方面没有观察到显著差异.
- 这些发现支持在医学教育中使用LLM生成的MCQ的可行性.
结论:
- 人工智能生成的MCQ在质量和难度上与人类生成的MCQ相似.
- 需要进一步的研究来确定AI内容与医学教育中专家审查的最佳整合.
- 未来的工作将集中在开发实践考试和评估其预测有效性.
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