Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions

Area of Science:

• Biomedical Engineering
• Computational Linguistics
• Speech Science

Background:

• Voice disorders affect millions globally, necessitating innovative diagnostic and monitoring tools.
• Traditional methods for voice analysis can be subjective and time-consuming.
• Artificial intelligence (AI) offers potential for objective, scalable, and sensitive voice assessment.

Purpose of the Study:

• To review current AI applications in voice disorder analysis.
• To highlight emerging AI technologies and their potential.
• To identify limitations and future directions for clinical integration of AI in speech pathology.

Main Methods:

• Comprehensive literature review of AI applications in voice disorder research.
• Analysis of current AI models, datasets, and methodologies.
• Synthesis of findings on challenges and opportunities for clinical implementation.

Main Results:

• AI-based voice analysis demonstrates high sensitivity to acoustic features for screening and monitoring vocal pathology.
• Current AI models are limited by small datasets and lack of standardized recording/reporting techniques.
• Emerging strategies like edge computing and EHR integration show potential for scalable, privacy-preserving clinical adoption.

Conclusions:

• AI holds significant promise for voice disorder assessment, offering enhanced accessibility and scalability.
• Addressing data limitations, standardization, and ethical considerations is crucial for clinical translation.
• Future research should focus on longitudinal, multimodal data, explainability, and validated implementation frameworks.