Artificial Intelligence in Otorhinolaryngology: Towards More Effective, Equitable, and Sustainable Healthcare

Abstract

Artificial Intelligence (AI) is progressively transforming medical practice, and otorhinolaryngology (ENT) is no exception. Based on the analysis of multimodal data, including imaging, endoscopy, audiometry, video, and clinical data - this specialty represents a particularly favorable field for the development of deep learning. Current applications include diagnosis, image classification, prediction, and selected therapeutic applications [1]. Large Language Models (LLMs) are also opening new perspectives in clinical, educational, administrative, and scientific domains [2]. In head and neck oncology, AI is being investigated for lesion detection and classification, radiological and histopathological analysis, tumor segmentation, and prognostic assessment [3, 4]. In otology, automated analysis of otoscopic images represents another promising application. A systematic review and meta-analysis demonstrated encouraging diagnostic performance for the classification of various ear diseases [5]. AI and machine learning are also being explored in audiology for the analysis of auditory data and hearing screening [6].

Citation

Philippe Gorce. Artificial Intelligence in Otorhinolaryngology: Towards More Effective, Equitable, and Sustainable Healthcare. WebLog J Otolaryngol. wjot.2026.i1806. https://doi.org/10.5281/zenodo.23105123