From ML Predictions to Informed Diagnostic Assistance Using the Toulmin Model of Argumentation
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arXiv:2607.09664v1 Announce Type: new 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…
1Key Takeaways
- arXiv:2607.09664v1 Announce Type: new 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….
2AIWedia Score
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv cs.AI reports that arXiv:2607.09664v1 Announce Type: new Abstract: To provide a structured and interpretable assessment, we decompose the image-based diagnosis into components following the Toulmin model of argumentation.
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