大型语言模型可以信任吗? 对围产期抑郁症的反应的可靠性和可读性常见问题
Jingyu Huang1, Hua Yu2, Junjian Chen3
1Faculty of Health Sciences, University of Macau, Taipa, China.
Frontiers in public health
|March 11, 2026
概括
大型语言模型 (LLM) 在提供关于围产期抑郁症的可靠信息方面表现有希望,但它们的可读性往往超过公共卫生素养水平. 为了公平的健康沟通,需要进一步改进.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 公共卫生传播 公共卫生传播
背景情况:
- 大型语言模型 (LLM) 在健康教育中越来越多地被使用,这引发了人们对人工智能生成内容的可靠性和可读性的担忧.
- 围产期抑郁症是一个重大的公共卫生问题,需要为受影响的个人提供可访问和准确的信息.
研究的目的:
- 评估五位领先的LLM对围产期抑郁症常见问题的答案的可靠性和可读性.
- 评估人工智能产生的内容的可读性是否符合公共卫生识字标准.
主要方法:
- 关于围产期抑郁症的27个常见问题被提出给ChatGPT-5,Gemini-2.5,微软Copilot,Grok4和DeepSeek.
- 两个产科医生独立评估了答案,使用验证的仪器 (DISCERN,EQIP,JAMA,GQS,HONCODE) 进行可靠性和六个可读性指数的评估.
- 使用类间相关系数 (ICC) 量化了评价者间的协议.
主要成果:
- 观察到高的互评分协议 (ICC 0.7290.847) 的情况. 在模型 (p < 0.001) 之间发现可靠性得分 (DISCERN,EQIP,HONCODE) 的显著差异.
- 在特定的质量构造中,Grok4,DeepSeek和Copilot表现出明显的优势. 然而,所有模型都超过了六年级推的阅读水平.
- 可读性得分始终低于基准,这表明健康素养较低的人可能面临挑战. 大多数模型提供了同情的内容,但缺乏完全的临床安全遵守.
结论:
- 在围产期抑郁症信息方面,LLM表现出中度至高可靠性,将其定位为潜在的补充资源.
- 可读性限制需要改进,以确保所有健康素养水平的可访问性.
- 提高可读性,来源归因和道德透明度对于最大化公共利益和实现公平的健康沟通至关重要.
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