疫苗信息的可信性如何在传统搜索引擎和基于AI的对话代理之间进行比较?
Amalie Dyda1, Kristian Stanceski2, Alexa Dakiniewich1
1The School of Public Health, University of Queensland, Brisbane, Queensland, Australia.
Public health
|August 2, 2025
概括
生成型人工智能和传统搜索引擎为健康信息提供了类似的可信度,但人工智能工具往往缺乏明确的来源和外部链接. 这影响了用户如何评估在线健康内容的可信度.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 生成型人工智能 (AI) 接口为获取健康信息提供了新的途径.
- 与传统搜索引擎相比,人工智能产生的健康内容的可信度在很大程度上仍未得到审查.
- 疫苗信息的可访问性和可信性对于公共卫生决策至关重要.
研究的目的:
- 将生成性AI接口提供的疫苗信息的可信度与传统搜索引擎进行比较.
- 评估特定的可信度指标,包括来源清晰度,信息最新性和额外资源提供.
主要方法:
- 对常见疫苗相关问题的答案进行了横截面内容分析.
- 在2023年12月从谷歌,Bing,Bard,ChatGPT (3.5和4.0) 和Claude AI中获取信息.
- 使用DISCERN仪器评估可信度,使用SHeLL编辑器测量可读性.
主要成果:
- 传统搜索引擎在信息来源的清晰度,最新性和提供额外资源方面超过了生成性AI (P < 0.001).
- 生成型人工智能接口在信息相关性 (P < 0.0001) 和整体质量 (P < 0.05) 方面表现出卓越的表现.
结论:
- 虽然整体可信度是可比的,但生成性人工智能工具经常遗漏关键的来源和外部链接信息.
- 生成型人工智能可能呈现出看似可信且易于阅读的信息,但缺乏标准可信度指标.
- 需要进一步开发以确保生成性AI接口提供透明和可验证的健康信息.
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