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.