A Comprehensive Systematic Review and Meta-Analysis: The Future of Artificial Intelligence in Diagnostic Otology — Opportunities and Barriers to Clinical Implementation

Introduction

Diagnostic otology relies on operator-dependent interpretation of otoscopy, temporal bone computed tomography (CT), audiometry and vestibular testing, and diagnostic accuracy outside specialist settings remains suboptimal. Artificial intelligence (AI) has been proposed as a scalable decision-support solution, but its pooled accuracy, robustness beyond development data, comparative value against clinicians and barriers to clinical implementation have not been synthesised across all diagnostic otology modalities. This review aimed to quantify the diagnostic performance of AI across otoscopic, radiological, audiological and vestibular applications and to map opportunities and barriers to implementation.

Methods