The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence

Area of Science:

• Computational Linguistics
• Biomedical Informatics
• Artificial Intelligence in Healthcare

Background:

• The human voice serves as a crucial communication medium and reflects emotional states.
• Vocal disruptions can be analyzed as potential biomarkers for disease diagnosis.
• Conversational artificial intelligence (AI) powers voice-enabled technologies for human-machine interaction.

Purpose of the Study:

• To review artificial intelligence (AI) models applied to vocal analysis for diagnostic purposes.
• To explore the outcomes and clinical applications of vocal biomarkers.
• To address ethical considerations, particularly data privacy and security.

Main Methods:

• Extraction of relevant vocal features from audio recordings.
• Analysis of extracted features using neural networks and machine learning (ML) models.
• Comparison of ML models against traditional spectral analysis techniques.

Main Results:

• Machine learning models demonstrate superiority over spectral analysis in integrating complex vocal feature data.
• Vocal biomarkers show potential in diagnosing neurological disorders (e.g., Parkinson's, Alzheimer's), psychological conditions, and other diseases (e.g., DM, CHF, CAD, GERD, pulmonary diseases, COVID-19).
• Encryption methods can mitigate privacy and security concerns associated with patient-identifiable vocal data.

Conclusions:

• AI-driven vocal analysis represents a promising non-invasive diagnostic tool.
• The integration of vocal biomarkers into healthcare can significantly enhance predictive analytics and disease management.
• Continued advancements in AI are expanding the potential of voice as a digital health solution, with privacy safeguards being paramount.