Advancing Otolaryngology Through Artificial Intelligence for Ear Disease Detection

Ear diseases remain a significant public health concern worldwide, with conditions such as otitis media, cholesteatoma, and tympanic membrane perforation contributing to hearing loss, communication difficulties, and reduced quality of life. Timely and accurate diagnosis is critical to preventing complications, yet conventional diagnostic methods in otolaryngology such as otoscopy, otoendoscopy, and imaging techniques often rely heavily on the expertise of specialists and are prone to variability between observers. Recently, artificial intelligence (AI) has emerged as a promising solution to address these limitations, particularly through the application of machine learning (ML) and deep learning (DL) techniques. This review surveys the current state of research focused on the classification and prediction of ear diseases using advanced computational approaches. Emphasis is placed on the evolution of model architectures, the emergence of otoscopic and otoendoscopic image datasets, evaluation methodologies, and the clinical applicability of automated systems. Special prominence is given to the shift from traditional ML models to modern DL frameworks, such as convolutional neural networks (CNN) and attention-based architectures, which demonstrate superior accuracy and generalizability. Additional strategies, including data augmentation, ensemble modeling, and incorporation of clinical metadata, are explored for their role in improving diagnostic precision. Synthesizing recent developments provides a comprehensive overview of how data-driven methods are transforming diagnostic workflows in otolaryngology. Ongoing challenges such as limited dataset diversity and model interpretability are also addressed, along with future directions for research, cross-disciplinary collaboration, and clinical deployment of AI in ear disease diagnosis.