在儿科透析中对大型语言模型进行比较评估:可靠性,质量和可读性
Esra Ensari1, Esra Nagehan Akyol Onder2, Pelin Ertan3
1Ministry of Health Antalya City Hospital, Department of Pediatric Nephrology, Antalya, Turkey.
概括
像ChatGPT,Gemini和Copilot这样的大型语言模型 (LLM) 为儿科透析常见问题提供可靠,高质量的答案. 然而,它们的高级可读性可能会限制公共卫生的影响,这表明需要更清晰的内容.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 卫生沟通健康沟通
背景情况:
- 广泛使用的大型语言模型 (LLM),包括ChatGPT,Gemini和Copilot,越来越多地用于健康信息.
- 评估这些LLM对于在专业医疗领域回答常见问题 (FAQ) 的实用性至关重要.
研究的目的:
- 评估来自三个领先的LLM对儿科透析常见问题的答案的可靠性,质量和可读性.
- 为了比较ChatGPT,Gemini和Copilot在提供可访问和准确信息方面的性能.
主要方法:
- 一组45个儿科透析常见问题被提出给ChatGPT,Gemini和Copilot.
- 使用修改后的DISCERN (mDISCERN) 尺度来评估可靠性.
- 使用全球质量评分 (GQS) 评估质量.
- 使用五个标准指标测量可读性:科尔曼-利乌指数 (CLI),简单测量Gobbledygook (SMOG),枪击雾指数 (GFI),Flesch阅读易度 (FRE) 和Flesch-Kincaid等级水平 (FKGL).
主要成果:
- 这三种LLM均表现出高可靠性,mDISCERN中位数为5.
- 各平台的质量评分 (GQS) 也很高 (中位数为5),尽管双子座的得分变化更大.
- 可读性分析表明,LLM生成的答案是以高级学术水平撰写的.
结论:
- LLM提供了对儿科透析常见问题的可靠和高质量的答案.
- 当前的LLM答案的高级可读性可能会成为公众理解的障碍.
- 提高内容可读性,可能通过专家审查,可以显著提高LLM产生的医疗信息的公共卫生影响.
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