Oropharyngeal cancer (opc) has become the leading site for human papillomavirus (hpv)–associated cancers in humans. It is an epidemic that remains relatively unfamiliar to most physicians, potentially delaying diagnosis and treatment. Traditionally, cancers involving the head and neck have occurred in smokers and in those with a significant alcohol history. Typically, hpv-positive opc presents in a younger, healthier population with a different set of risk factors and good prognosis for survival. However, many head-and-neck cancer patients, including those with hpv-positive disease, develop lifelong disabilities because of the morbid nature of their treatments, and those patients have the highest level of unmet needs in studies spanning cancer sites.    Knowledge of this epidemic, a high index of suspicion, and an understanding of how the tumours present in clinical practice can help physicians to make an early diagnosis, thus sparing the patient significant morbidity from treatments associated with more advanced disease stages. Furthermore, recognizing that these patients have distinct psychosocial needs and implementing a collaborative team approach is critical to providing optimal care and improving quality of life in the survivorship period.

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Objective Recent advances in artificial intelligence (AI) are driving innovative new health care solutions. We aim to review the state of the art of AI in otology and provide a discussion of work underway, current limitations, and future directions. Data Sources Two comprehensive databases, MEDLINE and EMBASE, were mined using a directed search strategy to identify all articles that applied AI to otology. Review Methods An initial abstract and title screening was completed. Exclusion criteria included nonavailable abstract and full text, language, and nonrelevance. References of included studies and relevant review articles were cross-checked to identify additional studies. Conclusion The database search identified 1374 articles. Abstract and title screening resulted in full-text retrieval of 96 articles. A total of N = 38 articles were retained. Applications of AI technologies involved the optimization of hearing aid technology (n = 5; 13% of all articles), speech enhancement technologies (n = 4; 11%), diagnosis and management of vestibular disorders (n = 11; 29%), prediction of sensorineural hearing loss outcomes (n = 9; 24%), interpretation of automatic brainstem responses (n = 5; 13%), and imaging modalities and image-processing techniques (n = 4; 10%). Publication counts of the included articles from each decade demonstrated a marked increase in interest in AI in recent years. Implications for Practice This review highlights several applications of AI that otologists and otolaryngologists alike should be aware of given the possibility of implementation in mainstream clinical practice. Although there remain significant ethical and regulatory challenges, AI powered systems offer great potential to shape how healthcare systems of the future operate and clinicians are key stakeholders in this process.

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