大型语言模型显示了可比的响应性能,但在关节镜检查患者问题上的可读性不同
Jeffrey S Mun1, Srish S Chenna2, Rachel L Poutre1
1Sports Medicine Center, Department of Orthopaedic Surgery, Mass General Brigham, Boston, Massachusetts, U.S.A.
Arthroscopy, sports medicine, and rehabilitation
|January 26, 2026
概括
大型语言模型 (LLM) 为关节镜问题提供了令人满意的答案,但需要澄清,并且有20%的错误答案率. 聊天GPT 聊天 在GPT 聊天
科学领域:
- 人工智能在医学中的应用
- 整形外科手术 整形外科手术
- 医疗信息质量 医疗信息质量
背景情况:
- 患者越来越多地使用人工智能获取健康信息.
- 评估人工智能产生的医疗内容至关重要.
研究的目的:
- 将LLM对关节镜检查的答案的质量和可读性进行比较.
- 确定LLMs的不正确响应率.
主要方法:
- 查询了3个LLM (ChatGPT,Co-Pilot,Gemini) 和10个关节镜常见问题.
- 通过使用利克特尺度来对准确性和清晰度进行分级的响应.
- 通过Flesch-Kincaid指标评估可读性.
主要成果:
- 在LLM中,准确度也相似 (ChatGPT: 3.0,微软: 2.9,双子座: 2.6).
- 没有LLM审查员的偏好 (ChatGPT: 2.0,微软: 2.1,双子座: 2.0).
- 总体而言,错误答案率为20%.
- 与双子座 (6.6) 和微软 (8.6) 相比,ChatGPT的可读性明显差 (11.0年级级) .
结论:
- 对于关节镜术的LLM反应是令人满意的,但需要适度的澄清.
- 实际上,LLM产生了大量的错误答案.
- 聊天GPT的可读性明显低于微软和双子座.
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