对与关节骨折相关的患者问题进行大型语言模型的比较评估:DeepSeek-V3-FW,Gemini 2.0 Flash和ChatGPT-4.5
Yejin Zhang1, Tao Huang1, Chaoran Liu1
1Department of Orthopaedics and Traumatology, The Chinese University of Hong Kong, Hong Kong, China.
Digital health
|February 2, 2026
概括
三种大型语言模型 (LLM) 在回答关节骨折管理问题时显示出高准确性和全面性. 它们的各种优势表明它们在医疗保健患者教育和临床决策支持方面可能发挥互补作用.
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
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地被采用,用于患者教育和临床决策支持.
- 在现实临床环境中对LLM进行系统的基准测试,特别是对于部骨折等高风险疾病,是有限的.
研究的目的:
- 评估和比较三个高级LLM的性能:DeepSeek-V3-FW,双子座2.0闪存和聊天GPT-4.5.5.
- 评估他们回答有关关节骨折管理的标准化患者问题的能力.
主要方法:
- 30个关于关节骨折管理 (一般知识,诊断,治疗,康复) 的标准化问题由骨科专家开发.
- 三个LLM产生了独立的响应,然后由经验丰富的骨科外科医生对其准确性和全面性进行评估.
- 统计分析包括克鲁斯卡尔-瓦利斯和千二测试.
主要成果:
- 所有LLM都表现出高可靠性,超过96%的回复被评为"好"或"优秀".
- 模型中的平均准确度得分相似,平均全面度为4.8/5.5.
- DeepSeek-V3-FW在结构化答案的一般知识方面表现出色;Gemini 2.0 Flash在诊断/康复方面带来了简短的答案;ChatGPT-4.5提供了对话式答案.
结论:
- 经过评估的LLM表现出强大的能力,能够提供关于关节骨折护理的准确和全面的信息.
- 它们的独特风格和特定领域的优势表明,在患者教育和临床支持中,它们可能具有互补的应用.
- 需要进一步的研究来确认安全性和将其整合到临床工作流程中的可行性.
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