使用微调的大型语言模型识别具有意想不到发现的高优先级放射学报告
Akihiro Umeno1, Mizuho Nishio2, Hidetoshi Matsuo1
1Department of Radiology, Kobe University Graduate School of Medicine, Chuo-ku, Japan.
European radiology
|December 24, 2025
概括
大型语言模型 (LLM) 可以准确预测高优先级的放射学报告. 精心调整的LLM显示了改善紧急发现的沟通和提高患者安全的潜力.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 放射学报告 放射学报告
背景情况:
- 准确及时识别高优先级 (HP) 放射学报告对于患者护理至关重要.
- 目前用于分类放射学报告的方法可能是资源密集的.
- 大型语言模型 (LLM) 为自动识别HP报告提供了一个潜在的解决方案.
研究的目的:
- 评估微调的LLM在预测高优先级放射学报告中的准确性.
- 为了比较不同LLM架构和输入配置的性能.
- 评估LLM在改善紧急放射性发现的传播方面的潜力.
主要方法:
- 四个LLM (Llama2 7B,Llama3 8B,Llama3 Elyza 8B,Llama 3.1 8B) 在1906年的放射学报告 (培训) 上进行了微调,并在176个报告上进行了测试.
- 报告被归类为高优先级 (HP) 基于高紧迫性或严重性的意外发现.
- 通过使用各种输入设置,对LLM进行了微调,包括仅发现,发现 + 转诊部门,发现 + 转诊部门 + 临床诊断 + 检查细节.
主要成果:
- Llama3 Elyza 8B,使用发现和参考部门作为输入,实现了最高的性能 (PRAUC=0.962,ROCAUC=0.968,精度=0.915).
- 微调的LLM在识别HP放射学报告方面表现出高准确度.
- 添加临床诊断或检查请求细节并没有持续改善性能.
结论:
- 精心调整的LLM可以准确预测高优先级的放射学报告.
- LLM显示出有很大的潜力,以支持在放射学中紧急发现的传播.
- 这项技术可以通过加快关键结果的传播来提高临床决策和患者安全.
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