从生成性AI评估基于证据的健康信息,使用与寻求查信息的非专业人士进行横截面研究
Felix G Rebitschek1,2, Alessandra Carella3, Silja Kohlrausch-Pazin1
1Harding Center for Risk Literacy, Faculty of Health Sciences Brandenburg, University of Potsdam, Potsdam, Germany.
NPJ digital medicine
|June 9, 2025
概括
大型语言模型 (LLM) 往往无法达到健康沟通标准. 即使有了更好的提示,LLM产生的健康信息质量仍然不足以进行知情决策.
科学领域:
- 卫生沟通健康沟通
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 越来越多地用于寻找健康信息.
- 基于证据的健康沟通需要提供最好的证据,以提供知情决策.
- 目前在健康沟通领域的LLM成绩并未得到充分理解.
研究的目的:
- 调查LLM遵守基于证据的健康沟通指南的快速依赖性.
- 评估一项最小的行为干预措施,以增强非专业人士的法学士健康信息提示.
- 评估LLM产生的健康信息的质量.
主要方法:
- 研究1:系统地改变及时信息,主题和LLM来评估准则的遵守.
- 研究2:在标准或增强提示条件下,随机将300名参与者分配给三个LLM.
- 盲目评价者使用两个评估工具评估了LLM的答案.
主要成果:
- 在研究1中,LLM未能满足基于证据的健康沟通标准.
- 响应质量在很大程度上取决于及时的信息.
- 非专业人士经常产生质量差的回应;简单的提示可以提高质量,但不能达到要求的标准.
结论:
- 目前,LLM作为独立的健康信息工具是不够的.
- 提高LLM推理,界面和用户提示技能至关重要.
- 整合LLM与基于证据的框架对于可靠的健康沟通至关重要.
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