Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions
- 1Sean Parker Institute for the Voice, Department of Otolaryngology‐Head and Neck Surgery Weill Cornell Medical College New York New York USA
Abstract
Objective To provide a comprehensive review of the current landscape of artificial intelligence (AI) applications in voice disorder, with emphasis on emerging applications, limitations, and future directions for clinical integration. Methods Literature review. Conclusion AI‐based voice analysis is a promising tool for the screening and monitoring of vocal pathology, offering advantages in accessibility, scalability, and sensitivity to subtle acoustic features. However, current models remain limited by small data sets and lack of standardization in recording and reporting techniques. To move beyond proof‐of‐concept studies, future work must focus on longitudinal and multimodal data integration, explainability, fairness, and validated frameworks for implementation. Emerging strategies such as edge computing, synthetic data, and ambient electronic health record (EHR) integration may facilitate scalable, privacy‐preserving adoption in clinical settings.
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BibTeX
@article{kutler2026artificial,
title = {{Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions}},
author = {Rachel B. Kutler and Anaïs Rameau},
journal = {World Journal of Otorhinolaryngology-Head and Neck Surgery},
year = {2026},
month = oct,
doi = {10.1002/wjo2.70103},
url = {https://doi.org/10.1002/wjo2.70103},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions
AU - Rachel B. Kutler
AU - Anaïs Rameau
PY - 2026
DA - 2026/10/23
JO - World Journal of Otorhinolaryngology-Head and Neck Surgery
DO - 10.1002/wjo2.70103
UR - https://doi.org/10.1002/wjo2.70103
AB - Objective To provide a comprehensive review of the current landscape of artificial intelligence (AI) applications in voice disorder, with emphasis on emerging applications, limitations, and future directions for clinical integration. Methods Literature review. Conclusion AI‐based voice analysis is a promising tool for the screening and monitoring of vocal pathology, offering advantages in accessibility, scalability, and sensitivity to subtle acoustic features. However, current models remain limited by small data sets and lack of standardization in recording and reporting techniques. To move beyond proof‐of‐concept studies, future work must focus on longitudinal and multimodal data integration, explainability, fairness, and validated frameworks for implementation. Emerging strategies such as edge computing, synthetic data, and ambient electronic health record (EHR) integration may facilitate scalable, privacy‐preserving adoption in clinical settings.
SN - 2589-1081
SN - 2095-8811
ER -