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专有和开源大型语言模型的比较评估,用于干预瘤学中系统的多源信息提取

Elif Can¹, Wibke Uller², Elmar Kotter²

¹Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany. elif.can@uniklinik-freiburg.de.

Cardiovascular and interventional radiology

|December 7, 2025

概括

与开源模型相比,专有的大型语言模型 (LLM) 如GPT-4o和Gemini在从肝细胞癌 (HCC) 患者的跨动脉化学栓塞 (TACE) 报告中提取临床数据方面表现出卓越的准确性. 然而,对于某些变量,专家的人类监督仍然至关重要.

科学领域:

人工智能在医学中的应用
瘤学信息学信息学
在医疗保健中的自然语言处理.

背景情况:

肝细胞癌 (HCC) 管理依赖于精确解释复杂的跨动脉化学栓塞 (TACE) 报告.
大型语言模型 (LLM) 显示了从非结构化文本中自动提取临床数据的潜力.
评估LLM在从TACE报告中提取特定变量的表现对于临床采用至关重要.

研究的目的:

为了比较专有 (GPT-4o,Gemini 1.5 Pro) 和开源 (Llama 3.1 70B,Llama 3.1 405B) LLMs 的性能.
评估LLM在从TACE报告中提取HCC患者的临床相关变量的准确性.
评估LLM在纵向数据提取中的稳定性.

主要方法:

追溯分析了来自50名HCC患者的556份TACE相关报告.
使用标准化提示符提取预定义的二进制和顺序变量.
通过混合效应回归,基于准确性,顺序分数和纵向错误率的性能评估.

主要成果:

专有LLM (GPT-4o,Gemini) 在对二进制和顺序变量的准确性方面明显优于开源LLM (Llama 3.1).
GPT-4o表现出最低的纵向误差率,表明随着时间的推移更大的稳定性.
所有评估的LLM在检测血管侵袭和后续评估方面表现不佳.

结论:

专有LLM可以准确地提取大多数TACE相关的变量,可能有助于干预瘤学的决策.
尽管对许多变量具有很高的准确性,但检测血管侵袭和随访的局限性需要持续的专家人类监督.
将LLM整合到临床工作流中需要仔细验证并考虑模型特定的优缺点.

关键词:

人工智能的人工智能是人工智能.肝细胞癌是肝细胞癌.干预性放射学是干预性的放射学.大型语言模型.长度临床数据长度临床数据自然语言处理自然语言处理.结构化的报告报告.通过动脉化学栓塞的超动脉化疗.

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