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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

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此摘要是机器生成的。

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.

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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.