Systematic Review Surveys XAI for Clinical Voice Analysis

According to the systematic review by Mohamed Ebraheem et al., published in J Med Internet Res (online Apr 20, 2026; PMID 42084850), the paper conducts a literature review of Explainable AI (XAI) methods applied to deep learning techniques in audio-based voice and speech clinical applications. The article's abstract states that audio-based voice and speech biomarkers are increasingly investigated as complementary or alternative clinical modalities, that adoption of deep learning has been motivated by superior performance, and that ethical and regulatory concerns about black-box models have limited clinical integration. The authors report that XAI has been employed to generate explanations for opaque model outputs (JMIR Preprints #83790 and ResearchGate preprint listings mirror the same review).

The review's stated focus on XAI for voice and speech models intersects two active technical areas: deep learning for audio and interpretability methods. Industry-pattern observations: practitioners commonly combine time-frequency representations (for example, spectrograms) with convolutional or transformer backbones, then apply post hoc XAI tools such as saliency maps, feature attribution, or surrogate models to produce human-readable explanations. Evaluating explanation quality for audio models remains an open engineering challenge because saliency overlays on spectrograms are less directly interpretable to clinicians than textual or tabular explanations.

Systematic reviews that map XAI methods onto a clinical signal modality are useful for regulatory, validation, and deployment conversations. Industry observers note that clinical adoption depends not only on raw performance but also on explainability, reproducibility, and alignment with clinical workflows. A curated literature map helps researchers identify methodological gaps, common evaluation metrics, and areas where benchmarking is lacking.

The review is a practical starting point for teams building or validating speech-biomarker models. Suggested areas of attention from broader community practice include transparent preprocessing pipelines, quantitative evaluation of explanations, and multimodal explainability that links audio-derived signals to clinical features.

• •Emergence of standardized benchmarks for XAI in audio-clinical tasks.
• •Work that quantifies clinical utility of specific explanation formats.
• •Regulatory guidance referencing explainability requirements for audio-derived biomarkers.