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大语言模型答案与互联网搜索常见问题的准确性 关于像葡萄糖一样的-1受体抗体治疗:探索性模拟研究
Sarah Ying Tse Tan1, Gerald Gui Ren Sng1, Phong Ching Lee1
1Department of Endocrinology, Singapore General Hospital, 20 College Road, Singapore, 169608, Singapore, +65 62223322.
JMIR formative research
|November 24, 2025
概括
大型语言模型 (LLM) 提供了比互联网搜索更客观和可重复的关于肥胖症的葡萄糖类-1受体激动剂 (GLP1RAs) 的信息. 然而,LLM需要更新数据,以应对新出现的GLP1RA问题.
科学领域:
- 数字健康数字健康
- 人工智能在医学中的应用
- 药理学 药理学是指药理学的学科.
背景情况:
- 新型葡萄糖类-1受体激动剂 (GLP1RAs) 越来越多地在网上讨论用于治疗肥胖.
- 在线信息可能包含关于GLP1RA使用的误解.
- 大型语言模型 (LLM) 为GLP1RAs患者教育提供了潜在的新途径.
研究的目的:
- 为了比较LLM (GPT-4o) 的准确性,客观性,相关性,可重现性和质量与GLP1RA治疗问题的互联网 (谷歌) 搜索答案.
- 评估LLM作为澄清患者关于GLP1RA治疗问题的工具.
主要方法:
- 17个关于GLP1RA治疗的模拟问题被提出给GPT-4o和谷歌搜索.
- 问题涉及GLP1RAs的指示,益处,治疗过程,副作用和风险.
- 两位独立专家对安全性,共识,客观性,可重复性,相关性和可解释性进行了评估,使用5分利克尔特等级.
主要成果:
- 与互联网搜索相比,LLM的回复在客观性和可重复性方面得分明显高.
- 在LLM和互联网搜索之间,在安全性,共识,相关性或可解释性方面没有发现显著差异.
- 互联网搜索产生了更多最新但异质的,有时商业偏见的信息,而LLM则缺乏对新出现的GLP1RA担忧的报道.
结论:
- 与互联网搜索相比,LLM提供了更客观和可重复的GLP1RA信息,具有相似的相关性.
- 法律法规要求更新数据集成,以解决新出现的GLP1RA问题.
- 人类监督和可读性评估对于安全和公平的LLM在肥胖护理中的使用至关重要.
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