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

Jul 14, 2026

Researchers propose a framework that decomposes ML-based retinal diagnosis into components of the Toulmin argumentation model: claim, grounds, warrant, qualifier, and rebuttal. A biomarker extraction model provides grounds, a MedGemma agent analyzes the warrant, and a qualifier is derived from quantitative evaluation. Rebuttals use image similarity via MedSigLip, with all components presented to human experts for assessment.

Why it matters: This approach enhances interpretability and trust in AI-assisted medical diagnosis by structuring ML outputs as arguments, enabling clinicians to critically evaluate AI-generated claims.

Full story at: arXiv AI/ML ↗