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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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在儿科发烧管理中评估大型语言模型:一个双层研究.

Guijun Yang1, Hejun Jiang1, Shuhua Yuan1

  • 1Department of Respiratory Medicine, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Frontiers in digital health
|September 19, 2025
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 进行了评估,以回答儿科发烧问题. 医生更喜欢ChatGPT模型,但亲属发现没有显著差异,强调需要更清晰的LLM沟通.

关键词:
医疗保健中的人工智能大型语言模型.医疗沟通 医疗沟通 医学沟通患者教育 患者教育儿科发烧 儿科发烧

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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科学领域:

  • 人工智能在医学中的应用
  • 儿科健康信息学 儿科健康信息学
  • 自然语言处理自然语言处理.

背景情况:

  • 儿童发烧是家长们常见的担忧,导致经常去看医生.
  • 大型语言模型 (LLM) 显示了医疗通信的潜力,但它们在儿科发烧中的使用尚未得到充分研究.
  • 评估LLM反应需要医疗专业人员和患者家属的观点.

研究的目的:

  • 在回答儿科发烧问题时比较四个LLM (ChatGPT3.5,ChatGPT4.0,YouChat,Perplexity) 的表现.
  • 评估医生和儿科患者的亲属如何评估LLM产生的答案.
  • 识别LLM对复杂儿科发烧查询的响应中的差异和相似之处.

主要方法:

  • 向四位LLM提出了30个与儿科发烧有关的问题.
  • 20名医生在四个预定义的维度中对LLM的反应进行了评分.
  • 儿科亲属评估了一组子集的反应,一些重新审视更深入的见解.
  • 使用统计分析,包括Tukey后期测试,以确定显著差异.

主要成果:

  • 医生评价ChatGPT3.5和ChatGPT4.0比YouChat和Perplexity在所有维度上都高.
  • 根据医生的说法,准确性是所有LLM的最高得分维度.
  • 儿科亲属没有发现LLM响应质量的显著差异.
  • 与亲属的再访表明,在理解和分析LLM成果方面存在挑战.

结论:

  • 基于互联网的LLM (YouChat,Perplexity) 并没有像预期的那样增强医疗问答能力.
  • 患者缺乏医学知识和LLM答案结构阻碍了理解.
  • 未来为患者使用的LLM开发应该优先考虑清晰,核心点和易于理解的语言.