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Manufacturing Process for Non-Adhesive Super-Soft Vocal Fold Models

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Beyond accuracy: The need for explainable AI in biomedical voice technology.

Rodrigo Capobianco Guido¹, Sylvio Barbon Junior²

¹São Paulo State University (UNESP), Institute of Biosciences, Letters, and Exact Sciences, Rua Cristóvão Colombo, 2265, Jardim Nazareth, São José do Rio Preto, 15054-000, São Paulo, Brazil.

Computers in Biology and Medicine

|May 16, 2025

Summary

This summary is machine-generated.

Speech analysis using artificial intelligence (AI) offers non-invasive biomarkers for various medical conditions. However, the interpretability of deep learning models remains a critical challenge for clinical trust and transparency.

Keywords:

Biomedical voice analysis Explainable artificial intelligence (XAI)Interpretable machine learning Speech-based diagnostics Vocal biomarkers

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Area of Science:

Explores the intersection of biomedical engineering, computer science, and clinical medicine.
Focuses on the application of advanced signal processing and machine learning in healthcare.

Background:

Speech and voice analysis are increasingly recognized as valuable non-invasive biomarkers.
These biomarkers aid in detecting and monitoring diverse medical conditions, including neurological, respiratory, and psychiatric disorders.
Artificial intelligence (AI) advancements have significantly enhanced the ability to detect subtle vocal pattern changes.

Discussion:

Deep learning models excel at identifying complex vocal biomarkers.
However, their 'black box' nature poses significant challenges for clinical adoption.
Interpretability is crucial for establishing trust and ensuring patient safety in medical applications.

Key Insights:

AI-powered voice analysis can detect subtle pathological vocal changes.
The clinical utility of these AI models is currently limited by a lack of transparency.
Addressing model interpretability is essential for translating AI in speech analysis to clinical practice.

Outlook:

Future research should prioritize developing interpretable AI models for speech analysis.
Enhancing transparency will facilitate the integration of AI-driven voice biomarkers into routine clinical diagnostics.
This integration promises to revolutionize early disease detection and patient monitoring.