Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment

Researchers propose Dys-XAI, an influence-based explainability framework that makes deep learning predictions for dysarthria severity assessment interpretable by linking decisions to similar training examples. The method uses gradient-based influence approximations to identify supportive and competing samples, with validation experiments confirming that removing influential samples systematically alters predictions, addressing a critical gap between model performance and clinical adoptability.

The intersection of clinical medicine and machine learning faces a persistent adoption barrier: high-performing models lack transparency. This research tackles dysarthria severity assessment, where manual perceptual ratings by clinicians remain the gold standard despite being subjective, time-consuming, and inconsistent across practitioners. Deep learning models can match or exceed human performance, yet their black-box nature prevents clinical integration because practitioners cannot audit or explain individual predictions to patients and stakeholders.

Dys-XAI introduces instance-level explainability through influence functions, a technique borrowed from robust machine learning that traces predictions back to influential training samples. Rather than generating abstract feature importance scores—which clinicians struggle to interpret—the framework identifies concrete reference cases that support or contradict each prediction. This approach bridges the interpretability gap by making explanations perceptually grounded: clinicians can compare a patient's assessment against similar documented cases.

The validation methodology strengthens credibility. Controlled deletion experiments removing top-5% to top-20% of influential samples demonstrate that predictions shift systematically, proving the identified samples genuinely impact model decisions rather than representing spurious correlations. This evidence-based validation differentiates the work from post-hoc rationalization approaches that sometimes generate plausible-sounding but disconnected explanations.

The impact extends beyond dysarthria assessment. This framework applies to any clinical prediction task where stakeholders require auditable decision pathways. Speech pathology, radiology, and diagnostic medicine broadly face identical transparency challenges. Success here establishes a template for deploying AI in regulated healthcare environments where regulatory bodies increasingly demand explainability not merely as nice-to-have but as mandatory compliance infrastructure for approval and reimbursement.

• →Influence-based explanations link predictions to interpretable training samples rather than abstract feature scores, enabling clinical adoption.
• →Gradient-based influence approximation provides computationally efficient instance-level explainability at scale.
• →Controlled deletion validation empirically proves identified samples genuinely impact model predictions.
• →The framework addresses the clinical transparency barrier preventing deep learning deployment in regulated healthcare settings.
• →Methodology is generalizable to other medical prediction tasks requiring auditable decision pathways.