用论证模型让AI诊断可解释，医生能看清依据和漏洞。
From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation
- 用图谱论证框架拆解AI诊断，分出论点、证据、理由等六要素。
- 医学知识代理评估理由可信度，相似度工具生成反驳依据。
- 适合医疗AI审评、临床辅助决策系统研发者参考。
为实现结构化且可解释的图像诊断评估，我们采用图谱论证模型（Toulmin Model of Argumentation）将基于图像的诊断分解为六个组成部分：论断、证据、理由、限定词、反例和背书。以视网膜疾病诊断为例，机器学习模型生成的论断并非直接接受，而是通过专用生物标志物提取模型提供证据，由具备医学知识的MedGemma代理分析理由是否成立；限定词根据理由与证据模型的整体量化评估结果确定；反例则通过MedSigLip计算图像相似性构建。所有组件共同呈现给临床专家，支持其对AI诊断做出更知情、更具批判性的判断。
原文摘要 · Abstract (English)
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.
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