大型语言模型作为现实世界的内科医学报告中的医疗错误的第二审核者:对开源和封闭源模型的前性比较研究
Roko Skrabic1, Ivan Viculin2, Zvonimir Boban3
1University Hospital of Split, Department for Nephrology, Split, Croatia.
International journal of medical informatics
|February 8, 2026
概括
开源大型语言模型 (LLM) 在内科医学中显示出"第二审核者"的潜力,有效地检测临床报告中的错误,并提供符合指南的建议,接近封闭模型的性能.
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:
- 临床文档中可以避免的错误会造成严重的患者安全风险.
- 开源大语言模型 (LLM) 作为临床文档审查者的实用性尚未得到充分证实.
研究的目的:
- 评估开源LLM在内科临床报告中识别和纠正错误方面的表现.
- 在错误检测和建议质量方面,将LLM绩效与人类临床医生进行比较.
主要方法:
- 分析了102份真实世界内部医学报告,其中包含了错误.
- 三个LLM (两个开源,一个封闭源) 负责错误本地化和纠正.
- 输出被盲目的内科专家评分,并与人类临床医生评论进行比较.
主要成果:
- 开源的LLM显示出强大的错误定位 (72.5%和65.7%) 和高推质量 (中位数为4/4).
- LLM经常标记了其他临床相关问题 (≥99%的报告).
- 评价者之间的一致性各不相同,GPT-OSS-120b在检测和质量方面表现出很好的一致性.
结论:
- 开源的LLM在识别临床文档中的错误方面,表现与封闭源模型相美.
- 在错误检测方面,LLM的表现优于人类临床医生,并提供了可靠的建议.
- 开源的LLM是保护隐私的临床决策支持工具的可信候选人.
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