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October 12, 2025Open Access

Artificial Intelligence in Otitis Media Diagnosis: A Review of Diagnostic Accuracy, Limitations, Clinical Integration and Future Directions

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This review evaluates AI-driven methods for diagnosing otitis media, highlighting their accuracy and clinical integration.

AI-driven models demonstrated high accuracy in detecting otitis media, with many studies showing sensitivity values exceeding 90%.
Deep learning techniques, particularly deep metric learning, significantly enhance the feature differentiation and classification performance.
The evaluation included performance metrics like accuracy and AUC-ROC, with values approaching 1.0 indicating strong diagnostic capability.
Challenges remain in dataset quality and model interpretability, indicating the need for future research focusing on real-world clinical validation.

Sulymon A. Saka (2025) studied this question.

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