源特征影响人工智能支持的骨科文本简化:未来的建议
Saman Andalib1, Sean S Solomon1, Bryce G Picton1
1University of California, Irvine, School of Medicine, Irvine, California.
JB & JS open access
|January 9, 2025
概括
大型语言模型 (LLM) 有效地简化了整形外科患者教育材料,GPT-4显示出最好的结果. 文本特征影响LLM简化成功,指导人工智能提高健康素养.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 医学教育 医学教育
背景情况:
- 骨科患者教育材料 (PEMs) 通常含有复杂的语言,妨碍患者的理解.
- 简化这些材料对于改善健康素养和患者结果至关重要.
- 大型语言模型 (LLM) 为文本简化提供了一个潜在的解决方案.
研究的目的:
- 评估各种LLM在简化骨科PEM的有效性.
- 通过LLMs识别预测成功文本转换的因素.
- 评估LLM对骨科患者材料可读性的影响.
主要方法:
- 通过使用GPT-4,GPT-3.5,Claude 2和Llama 2来转换48个骨科PEM.
- 在转换之前和之后,使用Flesch-Kincaid阅读易度 (FKRE) 和等级水平 (FKGL) 评分来测量可读性.
- 统计和机器学习方法分析了文本特征及其与转换成功的相关性.
主要成果:
- 所有测试的LLM都显著改善了FKRE和FKGL分数 (p < 0.01).
- GPT-4表现出卓越的性能,平均FKGL达到6.72±0.99.
- 转换成功受到原始文本特征的影响,如单词长度和句子复杂性,根据LLM而有所不同.
结论:
- LLM是简化复杂的骨科PEM的有效工具,提高了可读性.
- 在文本可读性方面,GPT-4表现出最显著的改进.
- 最初的文本特征是LLM转型成功的关键预测因素,为AI驱动的健康素养策略提供信息.
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