AI 中文总结
研究如何利用图尔敏模型为基于图像的诊断提供结构化评估，通过专门模型提取依据，医学知识智能体分析保证，基于定量评估确定限定词，用图像相似性度量构建反驳，辅助人类专家评估机器学习生成的诊断。
AI 中文摘要
为了提供结构化且可解释的评估，我们按照论证的图尔敏模型将基于图像的诊断分解为多个组件。该模型由主张、依据、保证、限定词、反驳和支持组成。对于机器学习模型生成的视网膜诊断主张，我们可以应用可解释人工智能方法或采用基于论证的方法。在我们的框架中，专门从图像中提取生物标志物的模型提供依据，由具备医学知识的智能体分析将依据与主张联系起来的保证，限定词基于保证和依据模型的整体定量评估确定，最后使用MedSigLip计算的图像相似性度量构建反驳。所有这些组件都呈现给人类专家，以便对机器学习生成的诊断进行更明智和批判性的评估。
英文摘要
To provide a structured and interpretable assessment, we decompose the image-based diagnosis into components following the Toulmin model of argumentation. This model consists of a claim, grounds, warrant, qualifier, rebuttal, and backing. Consider a claim generated by a machine learning (ML) model for retinal diagnosis. Rather than accepting this claim at face value, one could either apply explainable AI (XAI) methods or adopt an argumentation-based approach. In our framework, a model specialized in biomarker extraction from images provides the grounds. The warrant-linking the grounds to the claim - is analyzed by an agent equipped with medical knowledge; in our architecture, this role is fulfilled by a MedGemma agent. The qualifier is determined based on the overall quantitative evaluation of both the warrant and grounds models. Finally, a rebuttal is constructed using image similarity measures computed with MedSigLip. All these components are presented to the human expert, enabling a more informed and critical assessment of the ML-generated diagnosis.
