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

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Voice and speech biomarkers are increasingly used in medical applications, including pathology detection and mental health monitoring.
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AI-driven audio-based medical systems can enhance accessibility to healthcare for marginalized populations.
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The integration of AI in clinical settings is hindered by a lack of high-quality data for training reliable models.
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Explainable artificial intelligence (XAI) aims to improve the transparency of black-box models in healthcare.
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A multidisciplinary approach is essential for designing XAI methods that meet the diverse needs of stakeholders in clinical settings.

Journal Of Medical Internet Research (Jmir)

by Mohamed Ebraheem, Jamie Toghranegar, Bridge2AI-Voice Consortium, Yael Bensoussan, John Michael Templeton
June 24, 2026

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