从ChatGPT和CDC确保疫苗接种信息的准确性和公平性:混合方法跨语言评估
Saubhagya Joshi1, Eunbin Ha1, Andee Amaya1
1School of Communication & Information, Rutgers University, New Brunswick, NJ, United States.
JMIR formative research
|October 30, 2024
概括
像ChatGPT这样的大型语言模型 (LLM) 提供健康信息,但需要为不同人群提高可读性和语言公平性. 确保默认响应是准确和可理解的,对于明智的健康决策至关重要.
科学领域:
- 数字健康数字健康
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 大型语言模型 (LLM) 越来越多地用于健康信息,为克服传统访问障碍提供了机会.
- 然而,LLM产生的健康信息质量不一致,对非英语语言反应的研究有限.
- 这项研究通过评估跨语言的疫苗信息质量,重点关注健康公平.
研究的目的:
- 评估来自ChatGPT和疾病控制和预防中心 (CDC) 的英语和西班牙语疫苗接种信息的质量和语言公平性.
- 强调跨语言评估对于公平获取健康信息的必要性.
主要方法:
- 用英语和西班牙语对疫苗接种查询的ChatGPT和CDC响应的比较分析.
- 准确性,可读性 (弗莱什-金凯德等级) 和可理解性 (NIH PEMAT) 的定量和定性评估.
主要成果:
- 聊天GPT和CDC都提供了非常准确和可理解的答案 (得分>95%).
- 可读性得分,特别是在英语中,往往超过了推水平 (例如,ChatGPT English avg. 年级水平12.84与推 6).
- 疾病预防控制中心的回复显示了比ChatGPT更好的可读性;由于直接翻译,一些西班牙语的回复有不自然的措辞.
结论:
- 像ChatGPT这样的LLM对健康信息有希望,但需要提高可读性和语言公平性.
- 默认的LLM互动,通常由没有高级技能的用户进行,显著影响健康感知.
- 确保默认响应是准确的,可理解的,公平的,对于公共卫生和社区的知情决策至关重要.
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