对不同大型语言模型聊天机器人对有关神经阻塞的常见问题提供的响应质量的比较评估
Serkan Tulgar1, Can Aksu2, Onur Selvi3
1Department of Anesthesiology and Reanimation, Faculty of Medicine, Bahcesehir University, Goztepe Medicalpark Hospital, Istanbul, Türkiye, Turkey. serkantulgar.md@gmail.com.
BMC anesthesiology
|January 12, 2026
概括
聊天GPT和克劳德提供高质量的回答神经障碍常见问题,超过双子座和CoPilot. 这些大型语言模型 (LLM) 对医疗信息具有前景,但安全性和相关性需要仔细评估.
科学领域:
- 人工智能在医学中的应用
- 医疗信息检索 医疗信息检索
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 越来越多地被用作医疗保健信息来源.
- 现有研究评估了LLM对广泛医学主题的反应,但不是专门针对神经阻塞.
- 这项研究解决了理解神经阻塞相关查询LLM性能的差距.
研究的目的:
- 评估四个领先的LLM (ChatGPT,Claude,CoPilot,GEMINI) 对关于神经阻塞的常见问题 (FAQ) 的答案的质量.
- 通过使用已建立的质量评估工具和标准,比较这些LLM的性能.
主要方法:
- 一项前性研究,涉及使用Delphi方法开发评估工具和随后的调查.
- 十个神经阻塞相关的常见问题向四个LLM提出.
- 专家评级人员使用评估AI文本中的响应质量 (ARQuAT) 工具评估LLM响应,重点关注内容 (准确性,全面性,安全性,及时性,相关性) 和沟通 (可理解性,同情心,道德,可读性,中立性) 标准.
主要成果:
- 与GEMINI和CoPilot (p < 0.001) 相比,ChatGPT和Claude表现显著优越 (ARQuAT-整体得分) 的表现.
- 在内容和沟通质量方面,ChatGPT和Claude的满意度超过了80%,远远超过了GEMINI.
- CoPilot显示中等性能,而GEMINI显示低于平均水平的结果,特别是在安全性和相关性方面.
结论:
- 聊天GPT和克劳德为神经障碍常见问题提供了可接受到优秀的答案.
- CoPilot提供中等质量,而GEMINI的性能明显较低,特别是在安全性和相关性方面.
- LLM显示出回答神经阻断查询的潜力,但对其输出的仔细评估至关重要,特别是在患者安全方面.
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