Advances in Artificial Intelligence to Diagnose Otitis Media: State of the Art Review

Abstract Objective Otitis media (OM) is a model disease for developing, validating, and implementing artificial intelligence (AI) techniques. We aim to review the state of the art applications of AI used to diagnose OM in pediatric and adult populations. Data Sources Several comprehensive databases were searched to identify all articles that applied AI technologies to diagnose OM. Review Methods Relevant articles from January 2010 through May 2021 were identified by title and abstract. Articles were excluded if they did not discuss AI in conjunction with diagnosing OM. References of included studies and relevant review articles were cross‐referenced to identify any additional studies. Conclusion Title and abstract screening resulted in full‐text retrieval of 40 articles that met initial screening parameters. Of this total, secondary review articles (n = 7) and commentary‐based articles (n = 2) were removed, as were articles that did not specifically discuss AI and OM diagnosis (n = 5), leaving 25 articles for review. Applications of AI technologies specific to diagnosing OM included machine learning and natural language processing (n = 23) and prototype approaches (n = 2). Implications for Practice This review emphasizes the utility of AI techniques to automate and aid in diagnosing OM. Although these techniques are still in the development and testing stages, AI has the potential to improve the practice of otolaryngologists and primary care clinicians by increasing the efficiency and accuracy of diagnoses.