Deep Learning in Otolaryngology

Abstract

This narrative review found that DL applications in otolaryngology show potential for improving diagnostic performance, predicting outcomes, and providing intraoperative guidance. Widespread and equitable adoption needs to be supported by harmonized, high-quality, and representative datasets, as well as the mitigation of algorithmic bias and robust model interpretability. Federated learning and explainability are emerging frameworks that support the preservation of privacy and increased clinician trust. Standardized reporting, prospective validation, human-in-the-loop models, and interdisciplinary partnerships can help balance the promise of algorithmic approaches and their clinical utility, ensuring that DL tools contribute meaningfully to patient care.

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