基于语言模型的大型聊天机器人在败血性关节炎情景中的比较评估:ChatGPT,Claude和Perplexity
Hünkar Cagdas Bayrak1, Bekir Karagöz2, Özlem Bayrak3
1Department of Orthopaedics and Traumatology, Bursa Çekirge State Hospital, Bursa, Türkiye.
Acta orthopaedica et traumatologica turcica
|January 24, 2026
概括
像ChatGPT,Claude和Perplexity这样的大型语言模型 (LLM) 聊天机器人在治疗败血性关节炎的临床知识生成方面表现出很高的表现. 困惑在引用方面表现出色,而ChatGPT和Claude提供了更详细的内容,表明特定上下文的LLM选择至关重要.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 医学知识的产生 医学知识的产生
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地用于临床知识生成.
- 在专业医疗领域评估基于LLM的聊天机器人的性能对于安全有效的实施至关重要.
- 败血性关节炎带来复杂的诊断和管理挑战,使其成为评估临床AI的相关背景.
研究的目的:
- 为了比较评估三个领先的基于LLM的聊天机器人的临床知识生成性能:ChatGPT,Claude和Perplexity.
- 评估多个领域的聊天机器人性能,包括准确性,内容深度,临床适用性和参考支持.
- 根据专家评估,确定与败血性关节炎相关的临床场景的最佳LLM.
主要方法:
- 一项采用24个基于场景的临床问题进行的横截面比较研究,符合SANJO对败血性关节炎的指导方针.
- 来自ChatGPT (GPT-4),Claude 2和Perplexity AI的反应被整形外科和传染病专家独立评估.
- 使用5分利克尔特级别来评估科学准确性,内容深度,术语一致性,临床适用性,简洁性和参考支持方面的答案.
主要成果:
- 所有聊天机器人在准确性和术语一致性方面都取得了完美的成绩.
- 与ChatGPT和Claude相比,Perplexity AI表现出优越的参考支持,但内容深度较低.
- 聊天GPT比其他两个模型产生了更多不必要的阐述,而Perplexity获得了最高的整体得分.
结论:
- 基于LLM的聊天机器人具有很高的临床知识生成能力,但具有明显的优缺点.
- 选择LLM应该取决于上下文,优先考虑详细的解释 (ChatGPT,Claude) 或强大的引用 (困惑).
- 需要进一步的研究,以优化LLM整合到临床工作流程,如败血性关节炎等条件.
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