皮肤癌教育的质量和可读性从大型语言模型中脱离
Yanping Zhang1, Lei Wang2, Weiqiang Zhang1
1Department of Plastic Surgery, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, China.
Frontiers in public health
|March 9, 2026
概括
大型语言模型 (LLM) 提供健康信息,但质量和可读性被解. GPT-5显示了最高的整体质量,尽管没有一个AI模型在所有可读性指标上都表现出色.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 卫生沟通健康沟通
背景情况:
- 大型语言模型 (LLM) 越来越多地用于公共卫生信息检索.
- 在LLM生成的患者教育材料中,内容质量和可读性之间的关系尚未得到充分理解.
- 评估人工智能产生的健康内容对于确保准确和可访问的患者信息至关重要.
研究的目的:
- 通过五种不同的大型语言模型 (LLM) 生成的患者教育材料的质量和可读性进行基准测试.
- 评估LLM在生成关于皮肤癌常见问题 (FAQ) 的答案方面的表现.
- 为了确定内容质量和可读性在各种LLM之间之间的相关性.
主要方法:
- 评价了五个LLM (Doubao,DeepSeek,Wenxin Yiyan,Tongyi Qianwen和GPT-5) 的研究结果.
- 一个标准化的20个普通话中文皮肤癌常见问题集产生了100个总输出进行分析.
- 内容质量使用综合患者教育材料评估工具-患者报告的结果 (c-PEMAT-P) 和全球质量表 (GQS) 来衡量.
- 使用七个已建立的指数来评估可读性:ARI,FRES,GFOG,FKGL,CL,SMOG和LW.
主要成果:
- 在LLM中,表现出可比的可理解性和可操作性 (c-PEMAT-P).
- 总体质量 (GQS) 在模型之间有所不同,GPT-5获得最高分.
- 阅读度得分在模型和内容类型之间有很大的差异,没有一个模型在所有指标上都胜过其他模型.
- 发现内容质量和可读性在很大程度上是独立的.
结论:
- 高质量的AI产生的健康内容不能保证高可读性.
- 有效优化人工智能产生的患者教育,特别是皮肤癌信息,需要全面的方法.
- 战略应考虑LLM的选择和具体的内容主题,以确保质量和可访问性.
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