AGENTIC-SYSTEMSJuly 14, 20261 min read

From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation

Researchers have proposed a new approach based on the Toulmin model of argumentation to structure and interpret AI-based image diagnoses. By combining specialized models like MedGemma and MedSigLip, the framework allows medical experts to critically assess ML-generated diagnoses rather than accepting them at face value.

Computer Science > Artificial Intelligence

Title:From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation

View PDF HTML (experimental)Abstract: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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Source: arXiv cs.AI Recent

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