Medical data science in rhinology: Background and implications for clinicians

Young Joon Jun, Joonho Jung, Heung Man Lee

Research output: Contribution to journalReview article

Abstract

Background: An important challenge of big data is using complex information networks to provide useful clinical information. Recently, machine learning, and particularly deep learning, has enabled rapid advances in clinical practice. The application of artificial intelligence (AI) and machine learning (ML) in rhinology is an increasingly relevant topic. Purpose: We review the literature and provide a detailed overview of the recent advances in AI and ML as applied to rhinology. Also, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of these methods for clinical purposes. Methods: We aimed to identify and explain published studies on the use of AI and ML in rhinology based on PubMed, Scopus, and Google searches. The search string “nasal OR respiratory AND artificial intelligence OR machine learning” was used. Most of the studies covered areas of paranasal sinuses radiology, including allergic rhinitis, chronic rhinitis, computed tomography scans, and nasal cytology. Results: Cluster analysis and convolutional neural networks (CNNs) were mainly used in studies related to rhinology. AI is increasingly affecting healthcare research, and ML technology has been used in studies of chronic rhinitis and allergic rhinitis, providing some exciting new research modalities. Conclusion: AI is especially useful when there is no conclusive evidence to aid decision making. ML can help doctors make clinical decisions, but it does not entirely replace doctors. However, when critically evaluating studies using this technique, rhinologists must take into account the limitations of its applications and use.

Original languageEnglish
Article number102627
JournalAmerican Journal of Otolaryngology - Head and Neck Medicine and Surgery
Volume41
Issue number6
DOIs
Publication statusPublished - 2020 Nov 1

Keywords

  • Artificial intelligence
  • Data science
  • Deep learning
  • Otolaryngology
  • Rhinitis
  • Sinusitis

ASJC Scopus subject areas

  • Otorhinolaryngology

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