评估大型语言模型生成的糖尿病出院患者的健康教育材料:比较分析
Rongping Zha1, Jingbang Liu1, Li Wang1
1Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Journal of diabetes science and technology
|March 10, 2026
概括
大型语言模型 (LLM) 产生了准确的糖尿病教育材料,但可操作性和安全性需要改进. DeepSeek R1 显示出卓越的可理解性和安全性,这表明LLM是需要进一步开发的有价值的辅助工具.
科学领域:
- 医疗保健中的人工智能
- 糖尿病管理教育 糖尿病管理教育
- 数字健康干预措施 数字健康干预措施
背景情况:
- 持续的健康教育对于糖尿病自我管理至关重要.
- 大型语言模型 (LLM) 显示出产生糖尿病教育内容的潜力.
- 现有的法学士研究往往缺乏针对患者的临床形象量身定制.
研究的目的:
- 为了比较ChatGPT-4o,Doubao 1.5和DeepSeek R1在生成糖尿病健康教育材料方面的表现.
- 根据准确性,可理解性,可操作性,个性化,有效性和安全性来评估LLM生成的内容.
- 评估LLM的适用性,以创建为出院患者量身定制的糖尿病教育资源.
主要方法:
- 十个未识别的糖尿病患者的病历被用作了三个LLM的输入.
- 每个LLM都基于提供的临床数据生成了健康教育材料.
- 经验丰富的糖尿病护理专家使用预定义的标准评估生成材料的质量.
主要成果:
- 所有模型都实现了>70%的可理解性,其中DeepSeek R1表现最好 (P < .01).
- 所有模型的可操作性得分都低于70%,没有显著差异 (P > .01).
- 所有模型的准确性得分都很高 (≥98%);DeepSeek R1的安全性得分最高,而Doubao 1.5的安全性得分最低 (P < .01).
结论:
- 像ChatGPT-4o,Doubao 1.5和DeepSeek R1这样的LLM可以制作准确和可理解的糖尿病教育材料.
- 有关当前LLM产生的内容的可操作性和安全性存在重大担忧.
- 应利用LLM作为糖尿病教育的补充工具,需要进一步精细化个性化和可操作的患者内容.
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