Artificial Intelligence for the Otolaryngologist: A State of the Art Review

Andrés M. Bur, Matthew Shew, Jacob New

Research output: Contribution to journalReview articlepeer-review

40 Scopus citations


Objective: To provide a state of the art review of artificial intelligence (AI), including its subfields of machine learning and natural language processing, as it applies to otolaryngology and to discuss current applications, future impact, and limitations of these technologies. Data Sources: PubMed and Medline search engines. Review Methods: A structured search of the current literature was performed (up to and including September 2018). Search terms related to topics of AI in otolaryngology were identified and queried to identify relevant articles. Conclusions: AI is at the forefront of conversation in academic research and popular culture. In recent years, it has been touted for its potential to revolutionize health care delivery. Yet, to date, it has made few contributions to actual medical practice or patient care. Future adoption of AI technologies in otolaryngology practice may be hindered by misconceptions of what AI is and a fear that machine errors may compromise patient care. However, with potential clinical and economic benefits, it is vital for otolaryngologists to understand the principles and scope of AI. Implications for Practice: In the coming years, AI is likely to have a major impact on biomedical research and the practice of medicine. Otolaryngologists are key stakeholders in the development and clinical integration of meaningful AI technologies that will improve patient care. High-quality data collection is essential for the development of AI technologies, and otolaryngologists should seek opportunities to collaborate with data scientists to guide them toward the most impactful clinical questions.

Original languageEnglish
Pages (from-to)603-611
Number of pages9
JournalOtolaryngology - Head and Neck Surgery (United States)
Issue number4
StatePublished - Apr 1 2019


  • artificial intelligence
  • machine learning
  • natural language processing
  • otolaryngology practice


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