Aims: Artificial intelligence (AI) is rapidly reshaping diagnostic paradigms in otology, particularly through its application in radiological interpretation of middle and inner ear pathology. Artificial neural networks, particularly convolutional neural networks (CNNs) can be trained using large volumes of accurately labelled training data to automatically classify unlabelled test data with increasing accuracy. Despite rapid technological advances, its comparative clinical efficacy remains poorly defined. This systematic review aims to evaluate the clinical utility of artificial intelligence in CT and MRI interpretation for middle and inner ear pathology, focusing on its diagnostic accuracy and management decisions, in comparison to a clinician. Methodology: Systematic review with PRISMA guidelines, using seven online databases of articles examining artificial intelligence algorithms in otology. English language studies with primary or secondary endpoints pertaining to diagnostic efficiency were included. Editorials, non-clinical studies and literature reviews were excluded. Results: The database search identified 2124 articles. Titles and abstracts were reviewed for eligibility by two independent investigators. After full-text retrieval, 11 studies were included in this systematic review. Most applications of AI for interpretation of middle and inner ear radiological imaging included segmentation of CT and MRI scans and point-of-care diagnosis assistance. Meta-analysis of 11 studies assessing patient outcomes indicated high levels of accuracy of AI algorithms when compared to otolaryngologists and radiologists, with overall accuracy 92% vs. 85%, respectively. Conclusion: AI-assisted interpretation of radiological imaging shows strong potential to enhance otologic care by improving diagnostic accuracy, streamlining management, and expanding access to healthcare, especially in underserved areas.