评估大型语言模型在为中国患者提供患者教育方面的有效性:混合方法研究
1Jiangxi Medical College, The First Affiliated Hospital, Nanchang University, Nanchang, China.
Journal of medical Internet research
|April 10, 2025
概括
大型语言模型 (LLM) 显示出对教育患有眼球肌痛性肌痛症 (OMG) 的中国患者的希望. 聊天GPT o1-预览显示了高准确性和患者满意度,表明改善疾病管理的潜力.
科学领域:
- 人工智能在医学中的应用
- 数字健康数字健康
- 神经学 神经学
背景情况:
- 眼性肌痛性肌痛 (OMG) 是一种影响眼外肌肉的神经肌肉疾病,导致ptosis和diplopia.
- 中国有限的医疗保健资源阻碍了针对转基因的个性化患者教育.
- 大型语言模型 (LLM) 提供了可访问,人工智能驱动的健康信息的潜力.
研究的目的:
- 评估LLM在教育中国的转基因患者方面的有效性.
- 根据准确性,完整性,可读性,有用性和安全性来评估LLM在回答转基因相关问题的有效性.
- 分析患者对LLM可用性和可读性的评分.
主要方法:
- 阶段1: 5名LLM回答了130个眼科考试问题,与人类组进行了比较.
- 第1阶段:4个LLM回答了23个常见的OMG患者问题,由眼科医生评估.
- 第二阶段:20名OMG患者与2名LLM进行了互动,评估了满意度和可读性.
主要成果:
- 聊天GPT o1-预览在考试问题上获得了73%的准确性,超过了其他LLM和人群.
- 在患者问题上的正确性,完整性,帮助性和安全性方面,ChatGPT o1-preview得分最高.
- GEMINI提供了最易读的响应,而GPT-4o更复杂;患者满意度偏好了ChatGPT o1预览,而不是Ernie 3.5.5.
结论:
- 像ChatGPT o1-preview这样的LLM显示了提高眼球肌痛严重症患者教育的潜力.
- 解决错误信息,可读性和道德问题对于安全地将LLM纳入临床实践至关重要.
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