大型语言模型辅助非英语语言的外科手术同意表:内容分析和可读性评估
Namkee Oh1, Jongman Kim1, Sunghae Park1
1Department of Surgery, Samsung Medical Center, 81 Ilwonro, Seoul, Republic of Korea, 82 1093650277.
Journal of medical Internet research
|June 19, 2025
概括
大型语言模型 (LLM) 提高了外科同意表的可读性,但可能会减少关键的风险信息. 需要采取平衡的方法来确保人工智能辅助医疗文件的准确性和可访问性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者沟通 患者沟通
背景情况:
- 手术同意书中的复杂语言阻碍了患者的理解.
- 大型语言模型 (LLM) 提供了简化医疗信息的潜力.
- 关于LLM对非英语同意表格内容保存的影响的证据有限.
研究的目的:
- 评估LLM辅助编辑对韩国外科同意书的可读性和内容质量的影响.
- 具体评估多个机构标准化肝切除同意文件的变化.
主要方法:
- 收集了来自韩国7个机构的标准化肝切除同意书.
- 使用ChatGPT-4o和评估可读性 (KReaD,Natmal索引) 的简化表格.
- 通过专家审查和统计分析评估内容质量 (风险,益处,替代方案).
主要成果:
- 人工智能辅助编辑显著提高了可读性得分 (KReaD,Natmal),并减少了句子长度和难以理解的单词比例.
- 内容质量分析显示,风险描述和整体印象得分有所下降.
- 统计模型证实了风险披露和整体印象的显著减少,表明了潜在的遗漏.
结论:
- 通过LLM辅助的外科同意表格提高了可读性,但可能会损害内容的完整性,特别是风险披露.
- 平衡的方法至关重要,以保持可访问性,同时确保医疗和法律准确性.
- 进一步的研究应侧重于患者的理解,知情决策和多语言验证.
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