整形外科的大型语言模型的系统审查
Kevin Mo1, Rowen Lin2, Evan Dunn1
1Orthopaedic Surgery, Valley Hospital Medical Center, 620 Shadow Ln, Las Vegas, NV 89106, USA.
Journal of clinical medicine
|August 28, 2025
概括
大型语言模型 (LLM) 显示了骨科手术的潜力,但目前的AI准确性在评估中落后于骨科居民. 需要进一步开发临床应用.
科学领域:
- 整形外科
- 人工智能
- 医学教育
背景情况:
- 自2022年以来大型语言模型 (LLM) 的快速发展为骨科手术带来了新的机遇.
- 这次系统性审查是首次对现场LLM的研究格局进行审查.
研究的目的:
- 为了确定骨科研究的LLM.
- 评估它们的功能和结果的质量.
- 为了比较LLM与骨科实习生的表现.
主要方法:
- 使用PubMed,Embase和Cochrane图书馆进行的系统审查.
- 遵守系统审查和元分析 (PRISMA) 准则的优先报告项目.
- 包括60项评估LLM的研究,如ChatGPT,Bard,困惑AI和Bing.
主要成果:
- 与ChatGPT 3.5 (29. 4-55. 8%) 相比,ChatGPT 4.0的准确性更高 (47. 2-73. 6%没有图像).
- 巴德的准确性达到49.8-58%;基于图像的评估显示所有LLM的性能较低.
- 整形医生经常超过LLM,得分为74.2-75.3%.
结论:
- 在骨科评估准确性方面,ChatGPT 4.0 与ChatGPT 3.5 相比显著改善.
- 整形医生比现在的LLM得分更高.
- 在骨科评估,图像分析和临床文档方面有很大的机会提高LLM的表现.
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