使用大型语言模型对社交媒体上的青光眼治疗信息的评估
Asha Bulusu1,2, Paul R Cotran1,2, Amer M Alwreikat1,2
1Division of Ophthalmology, Department of Surgery, UMass Chan - Lahey School of Medicine, Burlington, MA, 01805.
Journal of glaucoma
|December 16, 2025
概括
玻璃眼专家发现,在关于玻璃眼治疗的社交媒体帖子中,不到一半是有利的. 虽然大型语言模型 (LLM) 的批判性较小,但它们与专家表示适度的同意,但难以识别低质量的内容.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 社交媒体平台越来越多地用于传播健康信息.
- 在这些平台上,健康信息的质量,特别是对于像玻璃眼这样的复杂疾病,有很大的差异.
- 评估这些内容的准确性,可读性,实用性和教育价值至关重要.
研究的目的:
- 评估在社交媒体上分享的青光眼治疗信息的质量.
- 为了比较由玻璃眼专家和大型语言模型 (LLM) 对社交媒体内容质量的评估.
主要方法:
- 五位玻璃眼专家使用五分利克特级别在四个领域对玻璃眼治疗的社交媒体帖子进行了评分.
- 基于专家的共识,建立了内容质量的参考标准.
- 一个大型语言模型 (GPT-4) 被提示使用相同的指令评估相同的帖子.
- 分析了专家和LLM之间的协议,使用统计措施 (科恩的卡帕).
主要成果:
- 玻璃眼专家认为,只有40%的关于玻璃眼治疗的社交媒体帖子是有利的.
- 在77%的案例中,LLM (GPT-4) 评价内容有利,比专家更频繁 (P=0.017).
- 在LLM和专家之间观察到适度的协议 (κ=0.421),主要是当内容被评为有利时.
结论:
- 虽然LLM对社交媒体内容质量的专家意见表示适度的同意,但他们对低质量的信息的辨别能力较低.
- 与人类专家相比,基于人工智能的系统可能会高估社交媒体上发现的健康信息的质量.
- 需要进一步的研究来完善人工智能工具,以准确评估在线医疗信息.
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