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在医疗纠纷中进行调解前咨询的大型语言模型:对人类专家进行比较评估

Min Seo Kim¹, Jung Su Lee², Hyuna Bae³

¹College of Medicine, Kangwon National University, Chuncheon, Korea.

Healthcare informatics research

|May 19, 2025

概括

使用链式思维管道的新大型语言模型 (LLM) 聊天机器人显著提高了医疗纠纷咨询质量,在信息,推理和满意度方面表现优于人类专家. 进一步的研究将完善其准确性,并探索患者接受度.

科学领域:

医疗法法律医疗法
人工智能的人工智能
医疗信息学医疗信息学

背景情况:

医疗纠纷评估需要专门的医疗和法律知识,这给寻求医疗不当索赔指导的患者带来了挑战.
医疗纠纷咨询的现有方法可能是复杂的,患者难以导航.

研究的目的:

开发和评估一个大型语言模型 (LLM) 聊天机器人,利用一条连锁思维管道用于医疗纠纷咨询.
为了比较聊天机器人的表现与人类专家在提供医疗纠纷咨询方面的反应.

主要方法:

使用了279个追溯医疗纠纷咨询案件的数据集,其中50个被随机选择用于验证.
克劳德 3.5 索内特模型通过五步思维链管道处理咨询请求.
三十八名专家通过四个维度的5分利克特级别对人类专家进行了聊天机器人的反应评估,并通过Wilcoxon签名等级测试进行了统计分析.

主要成果:

在信息质量 (p < 0.001),理解和推理 (p < 0.001) 以及整体用户满意度 (p < 0.001) 方面,LLM聊天机器人明显超过了人类专家.
聊天机器人更倾向于生成基于意见的内容 (p < 0.001).
尽管总体得分很高,但评估人员发现了聊天机器人面临挑战的具体局限性和领域.

结论:

一个基于思想链的LLM聊天机器人显示出增强医疗纠纷咨询服务的巨大潜力.
聊天机器人在关键绩效指标上表现优于人类专家,这表明它在这个领域的实用性.
未来的研究应该集中在缓解与法律和上下文细微差别相关的不准确性,评估患者采用,并优化特定领域的性能.

关键词:

咨询咨询咨询咨询咨询不同意和争议的意见和争议.大型语言模型错误的做法错误的做法自然语言处理自然语言处理.

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