医学学生认为哪些课程组件对评估AI输出最有帮助?
William J Waldock1, George Lam1, Ana Baptista1
1Imperial College School of Medicine, Imperial College London, Charing Cross Campus, London, W6 8RP, UK.
BMC medical education
|February 6, 2025
概括
医学学生可以以56%的准确度评估AI输出. 病理学和基于病例的培训是医疗保健中与大型语言模型 (LLM) 安全互动的关键.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 在医学教育中存在风险和机遇,因为它们具有类似人类的沟通能力.
- 未来的医生需要技能来批判性地评估人工智能产生的输出,以确保安全的患者管理.
- 调查医学学生评估LLM反应的能力对于调整培训至关重要.
研究的目的:
- 评估医学学生对临床场景的大型语言模型 (LLM) 反应的评估能力.
- 确定以前的学习经验,帮助学生仔细审查人工智能生成的医疗信息.
- 为了衡量未来医生对"临床快速工程"的认识.
主要方法:
- 对医学的最后一年的学生进行了一项调查,评估GPT 3.5对10个临床细节的反应的准确性.
- 对148名同意的医学学生的答案进行了内容分析.
- 学生们确定了培训,使得他们能够评估AI输出.
主要成果:
- 学生正确评估LLM输出的中位数准确率为56%.
- 基于案例的交互和利用问题的病理学教学被认为是最有效的培训.
- 对"临床快速工程"的熟悉度非常低,只有5%的学生知道这个概念.
结论:
- 病理学和基于案例的交互式教学对于为医学学生准备安全使用LLM输出至关重要.
- 医疗培训课程应该更新,为毕业生准备人工智能集成的医疗保健环境.
- 这项研究为AI时代设计有效的医学教育策略提供了见解.
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