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

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.