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通过大型语言模型简化放射学报告:符合隐私的开放式与封闭式权重模型
Annemarie Katharina Proff1,2, Babak Salam1,2, Mohammed Hayawi3
1Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
European radiology
|February 12, 2026
概括
大型语言模型 (LLM) 显著提高了患者对放射学报告的可读性. 符合隐私的开放式重量LLM,如Llama-3-70B,显示出临床使用的希望,尽管人类监督仍然至关重要.
科学领域:
- 医疗成像中的人工智能
- 在医疗保健中的自然语言处理.
- 放射学报告 简化
背景情况:
- 大型语言模型 (LLM) 提供了简化复杂医疗信息的潜力.
- 围绕封闭重量LLM的隐私问题限制了它们在医疗保健中的直接临床应用.
- 开发符合隐私的方法来生成患者友好的放射学报告至关重要.
研究的目的:
- 为了比较封闭重量和开放重量LLM在创建患者友好的放射学报告中的有效性.
- 评估医疗非专业人士的LLM生成报告的可读性和可理解性.
- 评估在医院部署的,符合隐私的开放式重量LLMs的临床适用性.
主要方法:
- 60份放射学报告 (X射线,超声波,CT,MRI) 使用GPT-4o (闭重) 和Llama-3-70b,Mixtral-8x22B (开重) 进行翻译.
- 使用Flesch阅读易度,阅读时间和单词/句子计数来评估可读性.
- 可理解性由21名医学专业人士在5分利克尔特尺度上进行评分;统计分析采用线性混合效应模型和H-Kruskal-Wallis测试.
主要成果:
- 与原始报告相比,LLM生成的报告显示出明显改善的可读性 (更高的Flesch分数) (p < 0.001).
- 在所有LLM报告中,非专业人士的可理解性评级显着更高 (p < 0.001),GPT-4o和Llama-3-70B的表现相似 (p = 0.136).
- 开放式重量模型 (Mixtral-8x22B,Llama-3-70B) 的潜在危害误差比GPT-4o的更多 (p=0.005,p=0.025).
结论:
- 士课程在很大程度上提高了非专业人士对放射学报告的理解.
- 符合隐私的,开放权重的LLM显示出临床整合的巨大潜力.
- 当使用LLM生成面向患者的医疗报告时,仍然需要人类监督.
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