大型语言模型的实用性,用于创建临床评估项目
George Lam1, Yusra Shammoon1, Anna Coulson1
1Imperial College School of Medicine, Imperial College London, London, UK.
Medical teacher
|August 26, 2024
概括
发电预训练转换器辅助 (GPT辅助) 临床和专业技能评估 (CPSA) 项目表现出与标准方法相比的质量,但成本要低得多. 在评估项目创建方面,GPT-assistance提供了大量的劳动力成本节省.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 评估设计 评估设计
背景情况:
- 创建临床和专业技能评估 (CPSA) 项目的传统方法是劳动密集型的.
- 评估新兴人工智能技术,如生成预训练变压器 (GPT) 对评估质量和成本的影响至关重要.
研究的目的:
- 为了比较学生的表现,考官的看法,和成本的GPT辅助CPSA项目与标准方法.
- 评估使用GPT开发医学教育评估工具的可行性和有效性.
主要方法:
- 一个前性的,受控的,双盲的比较GPT辅助和标准的CPSA项目.
- 两组六个实践案例被开发为最后一年的医学学生的形成性评估.
- 学生被随机分配到包含GPT辅助或标准案例的评估组.
主要成果:
- 在GPT辅助和标准项目之间,在项目难度或区分能力方面没有发现统计学上显著的差异.
- 审查员一致 (100%) 认为GPT辅助的案例是适当困难和现实的.
- 通过GPT的帮助,可以节省大量的劳动力成本,平均每病例减少57%.
结论:
- 通过GPT辅助的方法可以比传统方法更具成本效益地生产高质量的CPSA项目.
- 进一步的研究应该探索GPT在创建跨不同临床领域的CPSA材料的普遍性.
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