与人类教练相比,与大型语言模型的交互对临床诊断过程和医学四年级学生的结果的影响:使用患者简介进行前性随机实验的研究协议
Juliane E Kämmer1, Wolf E Hautz2, Gert Krummrey3
1Department of Emergency Medicine, Inselspital University Hospital Bern, University of Bern, Bern, Switzerland juliane.kaemmer@unibe.ch.
BMJ open
|July 18, 2024
概括
这项研究将像ChatGPT这样的大型语言模型 (LLM) 与用于医学诊断培训的人类教练进行比较. 这些发现将揭示AI.
科学领域:
- 医学教育和人工智能的人工智能
- 诊断决策支持系统 诊断决策支持系统
- 人与计算机在医疗保健中的互动
背景情况:
- 大型语言模型 (LLM) 提供了通过自然对话和广泛的知识获取来增强诊断决策的潜力.
- 关于LLM在临床环境中的影响存在不确定性,由于临床医生不熟悉,可能会过度依赖或使用不足.
- 临床医生对人工智能的一般态度可能会影响他们对基于LLM的诊断工具的批判性评估和采用.
研究的目的:
- 检查与LLM与人类教练的互动对诊断过程和结果的影响.
- 评估先前培训对与LLM或人类教练的互动的影响.
- 阐明在医疗诊断决策中使用人工智能 (AI) 的潜在好处和风险.
主要方法:
- 一个前性随机实验,涉及158名柏林Charité医学院第四年级医学学生.
- 参与者被分配与人类教练或ChatGPT进行互动,有或没有事先的培训 (学科间因素).
- 收集的数据包括信息搜索,假设数量,诊断准确度和信心;使用线性混合效应模型进行分析.
主要成果:
- 分析使用LLM与人类教练时的诊断准确性和信心.
- 评估特定培训干预措施对使用人工智能诊断辅助工具的影响.
- 在医学诊断中对AI的交互模式和态度的探索性分析.
结论:
- 结果将为AI工具 (如LLM) 在医疗培训和实践中进行最佳整合提供信息.
- 了解用户交互和培训需求对于最大限度地提高AI在诊断中的好处和减轻风险至关重要.
- 这项研究有助于更广泛地了解AI在提高临床决策和医学教育方面的作用.
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