Korean J Otorhinolaryngol-Head Neck Surg.  2020 Aug;63(8):341-349. 10.3342/kjorl-hns.2020.00633.

Application of Machine Learning in Rhinology: A State of the Art Review

  • 1Department of Otorhinolaryngology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea


The revolutionary development of artificial intelligence (AI) such as machine learning and deep learning have been one of the most important technology in many parts of industry, and also enhance huge changes in health care. The big data obtained from electrical medical records and digitalized images accelerated the application of AI technologies in medical fields. Machine learning techniques can deal with the complexity of big data which is difficult to apply traditional statistics. Recently, the deep learning techniques including convolutional neural network have been considered as a promising machine learning technique in medical imaging applications. In the era of precision medicine, otolaryngologists need to understand the potentialities, pitfalls and limitations of AI technology, and try to find opportunities to collaborate with data scientists. This article briefly introduce the basic concepts of machine learning and its techniques, and reviewed the current works on machine learning applications in the field of otolaryngology and rhinology.


Artificial intelligence; Deep learning; Machine learning; Medicine; Otolaryngology; Rhinology.
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