从自由文本计算机断层扫描报告中描述肝病变 - 一个现实世界的多中心分析
Jianliang Lu1, Keith Wan-Hang Chiu2, Chelsea Chan3
1Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong.
European journal of radiology
|January 24, 2026
概括
像Med-LM这样的医学上微调的大型语言模型 (LLMs) 在CT报告中对肝病变进行分类方面表现有前途,优于一般模型. 然而,高错误分类率和不一致的重复性目前限制了它们的临床应用.
科学领域:
- 医疗成像中的人工智能
- 临床数据的自然语言处理.
- 机器学习用于诊断支持
背景情况:
- 对医疗应用的大型语言模型 (LLM) 的评估至关重要.
- 评估了通用 (GPT-4) 和医疗微调 (Med-LM) 的LLM.
- 从非结构化计算机断层扫描 (CT) 报告中对肝病变的分类是重点.
研究的目的:
- 为了比较GPT-4和Med-LM在分类肝病变中的性能.
- 为了将LLM的成绩与放射科医生分配的LI-RADS分数进行比较.
- 分析快速优化对LLM分类准确性的影响.
主要方法:
- 利用了来自五个机构的296份连续的CT报告 (2014-2020年).
- 用于GPT-4和Med-LM的简单 (sp) 和优化的 (op) 提示.
- 与放射科医生LI-RADS评分和报告质量指标相比,对病变和患者水平的性能进行了基准测试.
主要成果:
- 特别是采用优化提示 (Med-LMop) 的Med-LM在所有分析中都表现出卓越的性能 (p < 0.001).
- 将病变划分为恶性和良性类别,对这两种模型都显著提高了准确性.
- 观察到高的非分类率 (12.7%-40.5%) 和可变的重复率 (12.7%-39.0%),特别是在良性病变.
结论:
- 在CT报告中的肝病变分类方面,Med-LM显著优于GPT-4.
- 这两种LLM都在检测恶性瘤方面表现出色,而不是完全LI-RADS分类.
- 临床效用目前受到高错误分类率和不一致的重复性限制.
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