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This systematic review aims to highlight the current role of AI in temporal bone imaging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Sources<\/jats:title><jats:p>A Systematic Review of English Publications searching MEDLINE (PubMed), COCHRANE Library, and EMBASE.<\/jats:p><\/jats:sec><jats:sec><jats:title>Review Methods<\/jats:title><jats:p>The search algorithm employed consisted of key items such as \u2018artificial intelligence,\u2019 \u2018machine learning,\u2019 \u2018deep learning,\u2019 \u2018neural network,\u2019 \u2018temporal bone,\u2019 and \u2018vestibular schwannoma.\u2019 Additionally, manual retrieval was conducted to capture any studies potentially missed in our initial search. All abstracts and full texts were screened based on our inclusion and exclusion criteria.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 72 studies were included. 95.8% were retrospective and 88.9% were based on internal databases. Approximately two\u2010thirds involved an AI\u2010to\u2010human comparison. Computed tomography (CT) was the imaging modality in 54.2% of the studies, with vestibular schwannoma (VS) being the most frequent study item (37.5%). Fifty\u2010eight out of 72 articles employed neural networks, with 72.2% using various types of convolutional neural network models. Quality assessment of the included publications yielded a mean score of 13.6\u2009\u00b1\u20092.5 on a 20\u2010point scale based on the CONSORT\u2010AI extension.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Current research data highlight AI's potential in enhancing diagnostic accuracy with faster results and decreased performance errors compared to those of clinicians, thus improving patient care. However, the shortcomings of the existing research, often marked by heterogeneity and variable quality, underscore the need for more standardized methodological approaches to ensure the consistency and reliability of future data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>NA <jats:italic>Laryngoscope<\/jats:italic>, 135:973\u2013981, 2025<\/jats:p><\/jats:sec>","DOI":"10.1002\/lary.31809","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T12:14:36Z","timestamp":1727784876000},"page":"973-981","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence in Temporal Bone Imaging: A Systematic Review"],"prefix":"10.1002","volume":"135","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6171-2947","authenticated-orcid":false,"given":"Dimitrios","family":"Spinos","sequence":"first","affiliation":[{"name":"South Warwickshire NHS Foundation Trust  Warwick UK"},{"name":"University Hospitals Birmingham NHS Foundation Trust  Birmingham UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasios","family":"Martinos","sequence":"additional","affiliation":[{"name":"National and Kapodistrian University of Athens School of Medicine  Athens Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dioni\u2010Pinelopi","family":"Petsiou","sequence":"additional","affiliation":[{"name":"National and Kapodistrian University of Athens School of Medicine  Athens Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nina","family":"Mistry","sequence":"additional","affiliation":[{"name":"Gloucestershire Hospitals NHS Foundation Trust, ENT, Head and Neck Surgery  Gloucester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7468-3287","authenticated-orcid":false,"given":"George","family":"Garas","sequence":"additional","affiliation":[{"name":"Surgical Innovation Centre, Department of Surgery and Cancer, Imperial College London St. Mary's Hospital  London UK"},{"name":"Athens Medical Center Marousi &amp; 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