基于互联网的患者教育材料的可读性,可靠性和质量分析以及关于梅尼耶氏病的大型语言模型
Salahaldin Alamleh1, Dorsa Mavedatnia2, Gizelle Francis3
1Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
概括
在线患者教育材料和Meniere病的大型语言模型输出缺乏可读性和质量. 大多数材料超过了推的读数水平,阻碍了患者的理解和可靠的健康信息获取.
科学领域:
- 医疗信息学 医疗信息学
- 患者教育 患者教育
- 医疗保健中的人工智能
背景情况:
- 在线患者教育材料 (PEM) 和大型语言模型 (LLM) 输出对于患者健康信息至关重要.
- 这些资源对Meniere病的可读性,质量和可靠性尚未得到充分证实.
研究的目的:
- 评估有关Meniere病的在线PEM和LLM产生的内容的可读性,质量和可靠性.
主要方法:
- 一项横截面研究分析了来自谷歌搜索的31个PEM和来自ChatGPT和Google Gemini的LLM输出.
- 使用五种经过验证的公式 (例如,Flesch阅读方便性,Flesch Kincaid等级水平) 评估可读性.
- 质量和可靠性被两个独立评级人员使用DISCERN工具进行评估.
主要成果:
- PEM的平均读数为10至12年级的阅读水平;LLM的输出是在研究生/大学水平.
- 只有16.1%的PEM符合美国医学协会 (AMA) 的六年级阅读水平指南;没有LLM输出.
- PEM和ChatGPT的输出被DISCERN评为"质量差",而谷歌双子座的输出被DISCERN评为"质量不错".
结论:
- 在线PEM和LLM对Meniere病的输出无法满足AMA的可读性标准.
- 这些资源通常质量和可靠性不佳,可能会影响患者的决策.
- 未来的PEM开发必须优先考虑增强可读性和信息完整性.
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