关于日本饮食补充剂的ChatGPT反应的有效性和可靠性:质量评估和内容分析
Mingxin Liu1, Tsuyoshi Okuhara2, Ritsuko Shirabe3
1Department of Health Communication, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
PEC innovation
|March 5, 2026
概括
像ChatGPT这样的大型语言模型 (LLM) 在食补充剂 (DS) 建议中显示谨慎但模糊,准确率为57%. 这凸显了消费者寻求健康指导的错误信息的风险.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 营养科学 营养科学
背景情况:
- 食补充剂 (DS) 是一个有争议的领域,容易产生错误信息.
- 大型语言模型 (LLM) 越来越多地用于健康信息检索.
- 对DS的LLM响应的准确性进行评估对于消费者安全至关重要.
研究的目的:
- 评估关于DS有效性的LLM响应的有效性和可靠性.
- 支持消费者对DS的知情决策.
- 为了指导改善与健康有关的查询的LLM绩效.
主要方法:
- 分析了30个DS和6种疾病的GPT-4和GPT-4o反应.
- 医疗专业人员使用基于证据的有效性指南评估了反应.
- 定性内容分析发现了模式和误导性信息.
主要成果:
- 在只有10%的案件中,LLM确认了DS的有效性;40%是"不确定的",50%是"不有效的".
- 在LLM中,答案准确率为57%,明显低于结构化营养任务.
- 内容分析显示了模板响应,模糊性和偶尔的不准确性.
结论:
- 关于DS的LLM回复是谨慎而又模两可的,存在中度的错误信息风险.
- 目前LLM的局限性可能会误导寻求健康建议的用户.
- 提高LLM基于证据的准确性和专业指导对于安全的AI辅助健康沟通至关重要.
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