From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation
Quick Answer
This paper applies the Toulmin model of argumentation to enhance interpretability in ML-based retinal diagnostics.
Quick Take
By integrating a MedGemma agent for medical knowledge and MedSigLip for image similarity, the framework enables critical assessment of ML claims, improving diagnostic accuracy and reliability for healthcare professionals.
Key Points
- Utilizes the Toulmin model to structure ML-generated retinal diagnosis assessments.
- Incorporates MedGemma for medical expertise and MedSigLip for image similarity analysis.
- Enables healthcare professionals to critically evaluate ML claims in diagnostics.
- Improves interpretability and reliability of machine learning predictions in medicine.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2607.09664 [cs.AI] |
| (or arXiv:2607.09664v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.09664 arXiv-issued DOI via DataCite |
Submission history
From: Adrian Groza [view email]
[v1]
Fri, 1 May 2026 06:58:17 UTC (2,895 KB)
— Originally published at arxiv.org
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