Clinical Report: Interpretable and Explainable Artificial Intelligence for Analyzing Voice and Speech in Healthcare

Overview

This systematic review explores the application of explainable artificial intelligence (XAI) in analyzing voice and speech for various healthcare applications. It highlights the challenges of integrating AI in clinical settings due to the 'black-box' nature of deep learning models.

Background

Voice and speech biomarkers have significant potential in medical applications, including disease diagnosis and monitoring. Traditional evaluation methods are often costly and invasive, limiting access to care. AI-driven solutions could enhance accessibility, but the integration of AI in clinical practice is hindered by the lack of high-quality data.

Data Highlights

No numerical data presented in the article.

Key Findings

• AI frameworks show promise in detecting cognitive decline through voice analysis.
• The 'black-box' nature of AI models poses significant barriers to clinical implementation.
• Explainable AI (XAI) aims to enhance transparency in AI decision-making.
• Multidisciplinary approaches are necessary for effective XAI design tailored to diverse stakeholders.
• Regulatory frameworks are evolving to address the integration of AI in healthcare.

Clinical Implications

Understanding the limitations and challenges of AI in clinical settings is crucial for healthcare providers.

Conclusion

The integration of explainable AI in voice and speech analysis presents both opportunities and challenges for healthcare.

Related Resources & Content

1. npj Digital Medicine, 2025 -- A systematic review of explainable artificial intelligence methods for speech-based cognitive decline detection
2. Journal of Medical Internet Research (JMIR), 2026 -- Beyond Time Saved: Implementation, Equity, and the Utility Threshold for Nursing AI Scribes
3. Journal of Medical Internet Research (JMIR), 2026 -- A Proposed Participatory Framework for Explainable AI in mHealth: Mixed Methods Study Integrating User and Stakeholder Requirements
4. Frontiers in Digital Health, 2026 -- Data-driven refinements for voice disorder classification: improving accuracy and generalisability
5. E6(R3) Good Clinical Practice (GCP) | FDA
6. Joint Commission and Coalition for Health AI (CHAI) Release Initial Guidance to Support Responsible AI Adoption Across U.S. Health Systems | Joint Commission
7. D-480.954 Explainability of Artificial/Augmented Intelligence a | AMA
8. Artificial Intelligence in healthcare - Public Health - European Commission
9. E6(R3) Good Clinical Practice (GCP) | FDA
10. Joint Commission and Coalition for Health AI (CHAI) Release Initial Guidance to Support Responsible AI Adoption Across U.S. Health Systems | Joint Commission
11. D-480.954 Explainability of Artificial/Augmented Intelligence a | AMA
12. Artificial Intelligence in healthcare - Public Health - European Commission
13. A systematic review of explainable artificial intelligence methods for speech-based cognitive decline detection | npj Digital Medicine
14. Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: A Systematic Review - PubMed
15. Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression - PMC
16. Voice Assessment and Vocal Biomarkers in Heart Failure: A Systematic Review - PMC
17. CONSORT 2025 statement: updated guideline for reporting randomised trials | The BMJ
18. Regulatory considerations for successful implementation of digital endpoints in clinical trials for drug development | npj Digital Medicine
19. Responsible AI Guidance - Blueprint for Trustworthy AI | CHAI