Explainable AI for Speech-Based Cognitive Decline Detection: A Field on the Brink of Clinical Utility

by ChatGPT-5.1

The article—A systematic review of explainable artificial intelligence methods for speech-based cognitive decline detection—offers the most comprehensive and up-to-date mapping of how explainability is being embedded into AI models that analyse speech for early detection of Alzheimer’s disease and related dementias. It is a timely review: dementia rates are rising sharply, early detection remains costly and inaccessible, and regulatory regimes such as the GDPR, the EU Medical Device Regulation (MDR), and the FDA’s AI/ML device guidance increasingly require transparent, clinically interpretable AI.

This systematic review synthesizes 13 studies published between 2021 and 2025 that applied explainable AI (XAI) methods—SHAP, LIME, attention mechanisms, rule extraction, and more—to acoustic, linguistic, and multimodal speech-analysis pipelines. The review traces the entire workflow from audio acquisition to clinical interpretation (illustrated well in the workflow diagram on page 4), and highlights both the promise and the persistent gaps in clinical readiness.

The State of the Evidence

The reviewed studies demonstrate that speech-based AI models can reliably detect cognitive decline with AUC values ranging from 0.76 to 0.94, depending on architecture and dataset (performance data summarized on pp. 5–7). Acoustic features such as pause duration, speech rate, articulation clarity, jitter, shimmer, and MFCCs consistently emerged as predictive across multiple studies. Linguistic features—including lexical diversity, pronoun usage, syntactic complexity, and discourse coherence—were equally prominent. These findings are broadly aligned with clinical literature on early dementia markers.

Yet the most significant contribution of the article is its exploration of how XAI methods illuminate these features. SHAP, in particular, proved crucial for mapping model predictions to clinically interpretable speech markers and was the most frequently deployed method (7 of 13 studies). Attention mechanisms provided insight into temporal dynamics—highlighting where in a speech segment the model “paid attention”—while LIME offered lighter-weight, model-agnostic local explanations.

Still, the studies exhibit considerable methodological heterogeneity—across tasks, datasets, preprocessing pipelines, model types, evaluation frameworks, and explanation formats—preventing meta-analysis and complicating generalization. The PRISMA diagram on page 3 underscores how few studies meet rigorous XAI inclusion thresholds (13 out of 2,077 screened).

The Most Significant Findings

1. Convergence on Key Speech Biomarkers

Despite dataset and model variability, XAI produces convergent findings:

• Pause patterns: increased frequency and duration

• Reduced speech rate

• Lower lexical diversity

• Higher pronoun usage

• Decreased pitch variability

These are robust across seven independent studies using distinct XAI techniques. The explanation stability scores (correlations between 0.65 and 0.82) suggest moderate reliability but still leave room for variance that could mislead clinicians.

2. SHAP as the Dominant and Most Clinically Aligned XAI Method

SHAP uniquely offers:

• Global feature importance

• Instance-level explanations

• Visualisations such as summary and waterfall plots

• Clinical alignment with speech-language pathology frameworks

Yet SHAP’s computational cost and occasional instability are barriers for real-time or embedded clinical systems.

3. A Striking Lack of Stakeholder Engagement

The Clinical Adoption Readiness Assessment on page 6 reveals:

• 92% of studies did not involve clinicians, patients, or end-users.

• 0% provided training materials.

• Only 2 studies scored above 3/5 on clinical readiness.

This gap is arguably the largest obstacle to genuine clinical deployment.

4. Dataset Limitations

Most studies rely heavily on ADReSS/ADReSSo or DementiaBank—public, English-language, relatively homogeneous datasets. The median sample size is only 162 participants. Several studies draw from the exact same subset of 156 participants. This raises concerns about overfitting, publication duplication, lack of diversity, and limited real-world transferability.

5. Clinical Validation is Still Rare

Only three studies conducted expert review. Only one evaluated explanations with multiple stakeholder groups. None reported deployment outcomes.

Strengths of the Review

The review’s key strengths lie in its methodological clarity and comprehensive synthesis. It follows PRISMA rigorously, includes preprints where appropriate, evaluates both technical and clinical dimensions of explainability, and provides tables that map XAI technique → model → feature set → evaluation method with exemplary clarity (Tables 1 and 2 on pages 6–8). It also includes a QUADAS-2 assessment (p. 9) that shows overall low bias in index-test methodology but high variability in participant selection and reference standards.

The figures—especially the workflow diagram—clearly situate XAI within a clinical pipeline and highlight where interpretability is introduced and how it flows toward eventual clinical decision support.

Limitations of the Field (as Identified in the Review)

1. Lack of Standardized XAI Evaluation Methods

Only five studies attempted formal XAI evaluation; most used ad-hoc measures.

2. Difficulty Interpreting Technical Acoustic Features

Clinicians cannot meaningfully interpret MFCCs, spectral centroid, harmonic-to-noise ratio, or many of the spectral measures that models rely on. Without clinical translations, these features are mathematically valid but practically useless.

3. Inadequate Consideration of Real-World Settings

• No longitudinal studies

• No large multi-site clinical validation

• Few telemedicine or mobile environments

• Limited attention to privacy, security, or regulatory compliance

4. Integration Into Clinical Workflows is Essentially Unexplored

Only two studies mentioned EHR integration. Most explanations are visual (bar charts, SHAP plots), with few textual summaries or hybrid interfaces.

5. Regulatory Readiness is Mostly Absent

Despite frequent references to GDPR and MDR, few studies address how their explanation mechanisms satisfy regulatory explainability, risk documentation, or safety monitoring expectations.

What This Means for the Future

Based on this review, speech-based XAI systems are promising but not yet clinically deployable as standalone diagnostic tools. They are best positioned for three near-term use cases:

1. Screening Support
   Flagging individuals for further evaluation with clear explanations of risk-associated speech markers.

2. Longitudinal Monitoring
   Tracking changes in linguistic and acoustic features over time, if explanation consistency can be improved.

3. Biomarker Discovery
   Identifying new or under-recognized speech features associated with dementia progression.

To reach mature clinical use, the field must solve three major challenges:

• Interpretation — Convert mathematical features into clinically meaningful constructs.

• Validation — Prove utility and safety across diverse populations and settings.

• Integration — Embed explainable outputs seamlessly into clinical workflows and EHRs.

Recommendations

For Researchers

• Collaborate with clinicians from the outset.

• Establish standardized XAI metrics: fidelity, stability, consistency, clinical utility.

• Develop hierarchical explanations for different audiences (clinicians, patients, regulators).

• Expand datasets to include diverse languages and naturalistic speech.

• Validate models in longitudinal real-world environments.

For Clinicians

• Demand transparent explanations, not only high accuracy scores.

• Participate in XAI usability studies to shape tool design.

• Learn the basics of model interpretability and its limitations.

• Treat XAI as a supplementary tool, not a diagnostic authority.

For Policymakers and Regulators

• Set minimum standards for explainability in medical AI.

• Define clear requirements for XAI documentation, safety monitoring, and liability.

• Support dataset development and multi-site clinical validation efforts.

• Encourage evaluations that include diverse linguistic and cultural contexts.

For AI Developers

• Design for explainability from the beginning, not as a patch on a black box.

• Use explanation formats that correspond to what clinicians already assess (e.g., fluency, coherence).

• Provide training materials and uncertainty estimates.

• Implement safeguards against over-reliance and misinterpretation.

Conclusion

This systematic review provides the clearest evidence to date that explainable AI can illuminate speech-based biomarkers of cognitive decline with significant clinical potential. But the field has not yet matured to the point of deployment. The gap is not technical—it is human. Without clinician engagement, workflow integration, standardized evaluation, and real-world validation, even the most sophisticated XAI method remains a research artefact rather than a medical tool.

The review maps a path forward: hybrid models combining interpretable linguistic features with powerful deep-learning embeddings; explanation formats aligned with clinical reasoning; and participatory design processes that ensure tools are built not just for clinicians, but with them.

The promise is real, the foundation is strong, and the direction is clear—what remains is the work of translation from research innovation to clinical reality.