大型语言模型的实用性和局限性,以简化在线内容的通用状牛皮
Samer Wahood1, Benjamin Gallo Marin2, Omar Alani3
1The Warren Alpert Medical School of Brown University, Providence, RI.
Rhode Island medical journal (2013)
|October 28, 2025
概括
人工智能 (AI) 大语言模型 (LLM) 改善了通用性性牛皮 (GPP) 在线健康信息的可读性. 然而,人工智能工具并没有保持原始内容的可靠性和质量,这表明患者教育需要谨慎.
科学领域:
- 皮肤病学 皮肤病学
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 在线健康信息 (OHI) 关于泛性性牛皮 (GPP) 经常使用复杂的语言,超过六年级的阅读水平.
- 这种缺乏可读性阻碍了患者对关键健康信息的理解和参与.
研究的目的:
- 评估三种AI大语言模型 (LLM) - - ChatGPT-3.5,ChatGPT-4和Google Gemini - - 在简化与GPP相关的OHI方面的有效性.
- 评估LLM生成的内容是否保持了原始源材料的可靠性和质量.
主要方法:
- 从GPP的前20个在线搜索结果中确定了文本.
- LLMs将这些文本改写为六年级的阅读水平.
- 使用可读性指数和增强的DISCERN仪器来评估可读性.
主要成果:
- 所有测试的LLM都显著改善了文本可读性 (p<0.01).
- 然而,与原始文本相比,LLM生成的内容在增强的DISCERN仪器上得分较低 (p<0.01),表明可靠性和质量下降.
- 统计分析证实了可读性和DISCERN分数的显著差异.
结论:
- 虽然AI LLM可以提高皮肤学OHI的可读性,但它们不能保持原始内容的可靠性和质量.
- 这些发现表明,在使用LLM用于皮肤病患者教育时,需要谨慎.
- 需要进一步的研究来优化LLM的使用,以便准确可靠地传播患者信息.
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