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适应性诊断推理框架用于病理学与多式联络大型语言模型

Yunqi Hong¹, Kuei-Chun Kao¹, Liam Edwards²

¹Computer Science Department, University of California, Los Angeles, CA, USA.

Communications medicine

|March 7, 2026

概括

这项研究介绍了病理学的透明人工智能 (AI) 框架. 人工智能为诊断审计提供与证据相关的推理,增强对医疗人工智能系统的信任和临床采用.

科学领域:

医学诊断医学诊断医学诊断
人工智能的人工智能
病理学病理学病理学

背景情况:

人工智能在病理学中的临床应用受到不透明的"黑子"系统的阻碍.
需要一个框架,为诊断审计提供透明,与证据相关的推理.

研究的目的:

开发一个框架,将不透明的AI模型转化为透明的系统.
产生与证据相关的推理,以支持病理学诊断审计.

主要方法:

利用现成的多式联通大型语言模型 (LLM) 进行主动诊断推理.
在没有更新模型重量的情况下,对乳腺癌和前列腺癌数据集采用了两阶段的自我学习过程.
来自病理学家的综合专家反,以使人工智能生成的标准与医疗标准保持一致.

主要成果:

在区分正常组织和侵袭性癌瘤方面,获得了超过90%的准确性.
通过识别关键组织学特征,成功地区分了诸如导管癌等复杂的亚型.
计算机生成的描述与专家病理学家的评估非常相匹配,在各种组织类型中显示出高性能和适应性.

结论:

该框架通过将视觉理解与推理结合起来,为临床可信的人工智能提供了一个有希望的方法.
这弥合了不透明的分类器和可审计系统之间的差距,为医学工作流程中的证据相关解释铺平了道路.

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