在大型语言模型时代的在线健康信息搜索:跨部门基于Web的调查研究研究
Hye Sun Yun1, Timothy Bickmore1
1Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States.
Journal of medical Internet research
|March 31, 2025
概括
年轻人正在使用基于大型语言模型 (LLM) 的聊天机器人来获取健康信息,但他们更谨慎地采取行动. 提高LLM准确性和透明度对于安全的在线健康信息搜索至关重要.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 基于大型语言模型 (LLM) 的聊天机器人越来越受欢迎,用于访问在线健康信息.
- 人们对基于LLM的聊天机器人提供的健康信息的准确性和安全性存在担忧.
- 了解用户对LLM产生的健康信息的看法和行为至关重要.
研究的目的:
- 调查寻求在线健康信息的用户的模式,感知和行为,包括基于LLM的聊天机器人.
- 检查在线健康信息搜索行为与社会人口统计学特征之间的关系.
主要方法:
- 一个基于网络的调查通过Prolific.通过群众工人管理.
- 该调查收集了有关社会人口统计,对医疗保健提供者的信任,电子健康素养,人工智能态度,慢性疾病和在线信息搜索行为 (来源类型,感知,行为) 的数据.
- 使用定量和定性分析来解释数据.
主要成果:
- 搜索引擎和健康网站是最常见的来源,21.2%的人使用基于LLM的聊天机器人 (ChatGPT,Copilot).
- 与其他来源相比,向基于LLM的聊天机器人寻求信息的用户的交叉检查率 (19.4%) 和遵守率 (48.4%) 较低.
- 使用LLM聊天机器人与年龄负相关,而积极的AI态度与咨询更多来源和使用LLM聊天机器人相关.
结论:
- 基于LLM的聊天机器人正在成为健康信息资源,特别是对于更年轻的用户来说,他们对人工智能的信任更高.
- 在各种在线健康信息来源中,感知质量和可靠性是相似的.
- 谨慎遵守LLM产生的健康建议表明需要提高LLM的准确性和透明度,以确保在医疗保健中负责任地使用.
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