Objective:

To evaluate the accuracy of artificial intelligence systems in detecting laryngeal disorders.

Approach:

Key Findings:

• AI models performed best in binary classification tasks, with accuracies ranging from 88% to 99% for distinguishing healthy from pathologic voices.
• Performance declined to approximately 70% to 90% for broader pathophysiologic categories and generally remained below 75% for specific disorders.
• AI performance varied by model architecture and data type, with traditional machine-learning achieving 88% to 96% accuracy for binary tasks and deep-learning systems achieving 97% to 99% on standardized datasets.
• Most studies relied on internal validation, with performance often declining by 10-20 percentage points on independent cohorts.

Interpretation:

The decline in performance from detection to diagnosis is attributed to acoustic overlap among laryngeal disorders, where distinct diseases can produce similar voice abnormalities.

Limitations:

• Many studies had methodological concerns, including dependence on limited databases, class imbalance, and lack of demographic diversity.
• Approximately 82% of studies used sustained-vowel tasks, which may not capture clinically relevant vocal variability.
• Fewer than 15% of studies shared source code or complete model documentation, limiting reproducibility.

Conclusion:

Current evidence supports AI primarily as a tool for screening, triage, and decision support rather than as an autonomous diagnostic system.

Sources:

• Journal of Personalized Medicine
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