可用的LLM平台如何成为随时可用的骨科参考来源? 一个比较分析
Girinivasan Chellamuthu1,2, Sathish Muthu1,2,3, Siddeshwar Siddamanickam1,4
1Orthopaedic Research Group, Coimbatore, India.
Indian journal of orthopaedics
|October 7, 2025
概括
人工智能 (AI) 大型语言模型 (LLM) 显示出在移动中 (OTG) 临床引用的前景,Bing Chat 显示出更好的证据水平. 在广泛采用医疗之前,需要进一步的定制.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 随时移动 (OTG) 引用对于及时的临床决策至关重要.
- 传统的参考资料 (教科书,期刊) 正在向数字化和人工智能驱动的平台发展.
- 大型语言模型 (LLM) 越来越多地用于临床环境中的快速信息检索.
研究的目的:
- 将LLM生成的答案的相关性和证据质量与OTG查询的人类临床医生参考进行比较.
- 评估ChatGPT (GPT-4),谷歌Bard和微软Bing Chat在提供临床信息方面的表现.
- 评估AI-LLM对于支持临床决策的适用性.
主要方法:
- 三个AI-LLM平台 (ChatGPT-4,Bing Chat,Google Bard) 进行了测试. 这些平台包括:
- 使用了250个带有已确定的答案和参考的骨科临床查询.
- 将LLM的答案与人类的答案进行了相关性和证据水平 (LOE) 的比较.
主要成果:
- 在测试的LLM中没有发现答案相关性的显著差异 (p=0.110).
- 聊天GPT-4在数值查询方面显示出优势 (p=0.006).
- 观察到答案的LOE有显著差异 (p<0.001),人类引用排名最高,其次是Bing Chat,ChatGPT-4和Bard.
结论:
- 与其他LLMs相比,微软BingChat在OTG问题上使用了相对更好的LOE.
- 所有评估的AI-LLM都显示出OTG在医疗保健中的引用潜力.
- 在临床实施之前,建议针对医疗领域进行定制,并遵守监管政策.
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