通过大型语言模型提高患者对心血管康复的教育
Som Singh1, Eric Errampalli2, Nathan Errampalli3
1University of Missouri-Kansas City, Kansas City, Missouri, and the University of Texas Health Science Center, Houston, Texas.
Missouri medicine
|February 17, 2025
概括
大型语言模型 (LLM) 显示出对教育患者进行心血管康复的前景. 然而,目前的LLM反应往往超过美国推的阅读水平,阻碍了患者的理解和坚持.
科学领域:
- 心血管健康 心血管健康
- 人工智能在医学中的应用
- 卫生识字 卫生识字
背景情况:
- 患者坚持进行心血管康复至关重要,但经常受到教育障碍的阻碍.
- 有效的患者教育是改善患者遵守心脏康复计划的关键因素.
- 大型语言模型 (LLM) 正在成为医疗保健中患者教育的潜在工具.
研究的目的:
- 评估LLMs为心血管康复产生的教育内容的可读性.
- 通过使用已建立的可读性指标来评估LLM生成的响应质量:枪击雾,Flesch Kincaid和Flesch阅读方便.
主要方法:
- 对LLM生成的对心血管康复查询的响应可读性得分的分析.
- 在ChatGPT 3.5,Copilot和Gemini中比较了枪雾,Flesch Kincaid和Flesch阅读易度的得分.
主要成果:
- 来自ChatGPT 3.5,Copilot和Gemini的可读性得分并不总是符合美国推的分级阅读水平.
- 与ChatGPT 3.5.5相比,Gemini和Copilot显示出更好的可读性.
- 总的来说,LLM的反应需要改进,以与目标患者的识字水平保持一致.
结论:
- LLM 作为心血管康复的教育资源具有潜力.
- 目前的LLM产生的内容需要提高可读性,以有效地作为患者教育工具.
- 提高文本可访问性对于最大限度地提高LLM在心脏康复患者教育中的实用性至关重要.
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