通过使用放射学中的大型推理模型推理结论,从诊断和解释方面的收益
Ruixin Wang1, Jinghang Wang1, Yisong Wang1
1Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China.
NPJ digital medicine
|December 31, 2025
概括
大型推理模型 (LRM) 通过提高完整性和透明度来增强瘤病例解释. 他们的推理过程在准确性和可解释性方面优于仅结论的格式和非推理模型.
科学领域:
- 医学成像分析 医学成像分析
- 在瘤学中使用人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 放射科医生可能会忽视影响癌症分期和治疗的关键次要发现.
- 大型推理模型 (LRM) 具有提高放射学解释完整性和透明度的潜力.
- 目前对LRM用于瘤成像解释的系统评估是有限的.
研究的目的:
- 为了比较LRM的推理过程与其仅结论输出和非推理模型的性能.
- 为了评估诊断错误和LRM输出的定性属性.
- 通过一个人-in-the-loop研究来评估LRM在放射学解释中的工作流影响.
主要方法:
- 这项研究涉及来自中国三家医院的900例多中心瘤病例.
- 一个LRM的推理过程的比较,只有结论的格式,和两个非推理模型.
- 三位高级放射科医生对诊断错误和定性属性的评估.
- 与六名放射科医生一起评估工作流效应的人在循环中的评估.
- 使用英语MIMIC-Cancer-90队列进行跨语言验证.
主要成果:
- 根据LRM的推理过程,错过/错误分类的错误最少,全面性,可解释性和公正性评级最高.
- 只有结论的LRM格式的性能下降;非推理模型在所有指标上表现不佳.
- 改善在各种癌症类型,成像方式,机构和语言中是一致的.
- 读者研究表明,感知完整性和推理清晰度有所提高,特别是在初级放射科医生中.
结论:
- 与其他方法相比,LRM推理过程显著提高了瘤成像解释的准确性和透明度.
- 虽然有效,但工作流集成需要优化,以解决相关成本并最大限度地提高临床效用.
- 在不同的临床环境和语言中,LRM显示出增强诊断性能和一致性的前景.
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