Clinical Report: Accuracy of AI Laryngeal Disorder Detection
Overview
Artificial intelligence systems show high accuracy in detecting abnormal voices but lower performance in identifying specific laryngeal disorders. An integrative review of 88 studies revealed that while binary classification tasks achieved accuracies of 88% to 99%, identifying specific disorders often fell below 75%.
Background
The ability to accurately detect laryngeal disorders is crucial for timely diagnosis and treatment. AI technologies have limitations in specificity that raise concerns. Understanding these limitations is essential for integrating AI into clinical practice effectively.
Data Highlights
No numerical data available.
Key Findings
- AI models excelled in binary classification tasks, achieving 88% to 99% accuracy for distinguishing healthy from pathologic voices.
- Performance declined to approximately 70% to 90% for broader pathophysiologic categories and remained below 75% for specific disorder identification.
- Only 7 out of 88 studies included both internal and external validation, with performance often decreasing by 10-20 percentage points on independent cohorts.
- Methodologic concerns included reliance on limited databases and a lack of demographic diversity in study populations.
- Approximately 82% of studies used sustained-vowel tasks, which may not fully represent clinically relevant vocal variability.
- AI is currently more suited for screening and decision support rather than as an autonomous diagnostic tool.
Clinical Implications
Clinicians should be aware of the limitations of AI in diagnosing specific laryngeal disorders and continue to rely on traditional assessment methods.
Conclusion
The review highlights the limitations of AI in voice disorder detection and the need for further research to improve diagnostic accuracy for specific laryngeal conditions.
Related Resources & Content
- Mairesse S., Journal of Personalized Medicine, 2025 -- Accuracy of AI Laryngeal Disorder Detection
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