Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review

Clinical Snapshot

Key Findings

• Effect Size: Not applicable — narrative systematic review without meta-analysis; no pooled effect sizes reported

• Primary Outcome: Characterisation of XAI methods applied to deep learning voice and speech analysis in clinical settings: 30 studies identified across six XAI method categories (gradient-based, perturbation-based, surrogate model-based, model-internal representation, concept-based detectors, attention-based explanations), spanning voice disorders, neurodegenerative diseases, psychiatric conditions, and traumatic brain injury

• Nnt Or Sensitivity: Not applicable for this review type; however, the review notes that most included studies reported model performance metrics (accuracy, AUC) without standardised external validation, limiting interpretation of diagnostic accuracy claims

• Confidence Interval: Not applicable — no quantitative synthesis performed

Clinical Application

Clinical deployment of XAI-enabled voice analysis systems is currently not feasible for routine practice. The review identifies that no included study conducted human-in-the-loop evaluation with clinical stakeholders, explanation validation on external datasets is largely absent, and most systems remain research prototypes. Regulatory pathways (FDA, TGA) for AI-based diagnostic tools require demonstrated clinical validity and explainability that current systems do not yet provide. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based Software as a Medical Device (SaMD) under the Medical Devices framework, with increasing scrutiny of black-box AI systems. The TGA's AI/ML-based SaMD guidance aligns with the review's finding that explainability is a prerequisite for clinical deployment. The RACGP has no current guidelines specifically addressing voice biomarker AI tools. The Australian Digital Health Agency's National Digital Health Strategy 2023–2028 emphasises trustworthy AI, making the XAI gaps identified in this review directly relevant to Australian health technology assessment. PBS listing of voice-based diagnostic tools is not currently applicable, but future reimbursement pathways would require the clinical validation evidence this review identifies as absent. Australian researchers at institutions such as the University of Melbourne, Macquarie University, and the Australian e-Health Research Centre are active in clinical speech pathology AI, making this review relevant to local research prioritisation. Clinicians and health informaticians working with patients who have voice disorders, neurodegenerative conditions (e.g., Parkinson's disease, ALS), psychiatric disorders (e.g., depression, schizophrenia), or traumatic brain injury, where voice and speech biomarkers are under investigation as diagnostic or monitoring tools

Abstract

BACKGROUND: Driven by recent advances in artificial intelligence (AI), particularly in medicine, audio-based voice and speech biomarkers are increasingly investigated for various medical applications as a complementary or even alternative modality to traditional medical devices. The adoption of deep learning techniques in recent literature is motivated by their superior performance compared to classical machine learning methods. However, ethical and regulatory concerns regarding the black-box nature of these models have limited their integration into clinical workflows. Consequently, explainable artificial intelligence (XAI) has recently been used to address this issue by generating explanations for opaque model outputs. Ideally, medical XAI systems aim to provide human-understandable, clinically grounded explanations essential for enhanced AI trustworthiness and, thereby, facilitate adoption into real-world clinical settings. OBJECTIVE: We conduct a systematic literature review of XAI methods applied for explaining deep learning techniques in audio-based voice and speech clinical applications. We aim to identify what XAI methods have been used to explain the decisions of deep learning voice and speech AI systems in health care, as well as XAI-informed insights. Additionally, we aim to contextualize these findings with respect to clinical applicability and stakeholder relevance. Lastly, we identify opportunities and recommendations for future clinical audio XAI design. METHODS: We used PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Six electronic databases (IEEE Xplore, ACM Digital Library, Scopus, PubMed, Web of Science, and Nature) were searched for papers published between January 2015 and February 2025. Eligible studies applied explainability or interpretability methods to deep learning models for voice or speech audio in health care contexts. Risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool). The results were thematically synthesized across explainability categories, input representations, clinical domains, validation strategies, and stakeholder considerations. RESULTS: A total of 30 studies met the inclusion criteria. These studies used a range of explainability approaches, including gradient-based methods, perturbation-based techniques, surrogate model-based methods, model-internal representation analyses, concept-based detectors, and attention-based explanations. Applications spanned diverse clinical domains, including voice disorders, neurodegenerative diseases, psychiatric conditions, and traumatic brain injury. Overall, results indicate that most studies relied primarily on qualitative interpretation of explainability outputs, with limited quantitative validation of explanation consistency across external datasets. Furthermore, none of the included studies explicitly conducted human-in-the-loop evaluations with relevant stakeholders, highlighting a substantial gap in stakeholder alignment. CONCLUSIONS: Current XAI practices in clinical voice and speech analysis are limited by insufficient validation, lack of domain-specific design, and misalignment with clinical stakeholder needs. This review highlights opportunities for developing validated, audio-aware, and stakeholder-centered XAI approaches to support trustworthy clinical deployment. Interpretation of these findings should consider limitations related to single-reviewer study selection, potential high-risk of bias, and the repeated use of benchmark datasets.

References

1. 1.Ebraheem, M., Toghranegar, J., Bensoussan, Y., & Templeton, J. M. (2026). Explainable and interpretable AI for voice and speech analysis in clinical care: Systematic review. Journal of Medical Internet Research. https://doi.org/10.2196/83790