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多模态大型语言模型与知识检索使用流程图嵌入形成对胰腺囊性病变的后续建议

Zheren Zhu¹, Jin Liu¹, Cheng William Hong¹

¹Department of Radiology and Biomedical Imaging, University of California, San Francisco. San Francisco, CA 94143.

AJR. American journal of roentgenology

|April 16, 2025

概括

一个多式大型语言模型 (LLM) 与嵌入流程图的流程图在建议胰腺囊性病变 (PCLs) 管理的随访中显著提高了准确性,遵守临床指南. 这种方法增强了对偶然发现的诊断决策支持.

科学领域:

放射学中的人工智能
医学成像分析医学成像分析
临床决策支持系统临床决策支持系统

背景情况:

美国放射学学院 (ACR) 偶然发现委员会 (IFC) 算法指导胰腺囊性损伤 (PCL) 管理.
实施用于PCL管理的LLM解决方案是复杂的,因为多模式数据要求 (图表,表格).

研究的目的:

评估使用流程图嵌入PCL管理建议的多式联通LLM方法.
评估LLM集成多式联运数据的能力,以便准确的后续安排.

主要方法:

提及PCL的腹部CT/MRI报告的回顾性研究 (2023年9月至2024年9月).
将发现部分输入到多模式的LLM (GPT-4o) 中,使用三个知识检索方法:默认,纯文本RAG和流程图嵌入.
使用放射科医生共识评估PCL特征的LLM准确性,随访建议和间隔变化评估.

主要成果:

使用流程图嵌入的LLM实现了PCL特征的98.0-99.0%准确性.
与其他方法 (p<.001) 相比,使用流程图嵌入 (89.9-91.9%) 显著提高了后续推准确性.
任务2显示PCL间隔变化的准确率为96.5%,调整后续时间表的准确率为81.2%.

结论:

嵌入多式流程图可以提高LLM在生成PCL管理后续建议的准确性.
该框架显示了使用各种临床指导文件对其他偶然发现的更广泛应用的潜力.

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