使用大型语言模型定制青光眼教育:解决患者理解的健康差异
Aidin C Spina1, Pirooz Fereydouni1, Jordan N Tang1
1School of Medicine, University of California, Irvine, Irvine, CA.
Medicine
|January 10, 2025
概括
像GPT-4这样的大型语言模型可以简化眼的复杂医学信息. 这项技术通过使科学文献在各种教育水平上更容易获得,提高了患者的理解.
科学领域:
- 眼科和医疗信息学
- 医疗保健中的人工智能
背景情况:
- 患者对医学文献的理解对于在绿眼的护理中做出明智决策至关重要.
- 现有的医学文本往往是患者理解的重要障碍,原因是复杂的术语和结构.
研究的目的:
- 评估GPT-4在简化医疗文献中的有效性.
- 评估GPT-4对患者教育材料可读性和内容一致性的影响.
主要方法:
- GPT-4将已发表的青光眼摘要 (n=62) 和患者教育材料 (n=9) 转换为五年级的阅读水平.
- 在六个不同的教育水平上,GPT-4产生了新的教育内容.
- 使用弗莱什金凯德等级水平 (FKGL) 和弗莱什阅读易度 (FKRE) 评分来测量可读性.
- 隐性语义分析 (LSA) 使用等号相似度评估了内容一致性.
主要成果:
- 抽象转换显著减少了30%的FKGL,并增加了66%的FKRE (P<.001).
- 患者教育材料显示,FKGL下降了28%,FKRE增加了19% (分别P=.0272和P=.0459).
- LSA证实了转换材料的高语义一致性,抽象的平均共因相似度为0.861,PEM的平均共因相似度为0.937.
结论:
- GPT-4有效地简化了对眼的复杂医疗信息,提高了患者的可访问性.
- 该模型保持了主题一致性,同时在各种教育水平上提高了可读性.
- GPT-4显示出作为一种在眼科中开发可访问的患者教育材料的工具的巨大潜力.
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