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Medical data science in rhinology: Background and implications for clinicians.
Jun, Young Joon; Jung, Joonho; Lee, Heung-Man.
Afiliação
  • Jun YJ; Department of Otorhinolaryngology, SoonChunHyang University Hospital, Gumi, South Korea.
  • Jung J; Innovation Technology Research Division, Gumi Electronic & Information Technology Research Institute, Gumi, South Korea.
  • Lee HM; Department of Otorhinolaryngology-Head and Neck Surgery, Guro Hospital, Korea University College of Medicine, Seoul, South Korea. Electronic address: lhman@korea.ac.kr.
Am J Otolaryngol ; 41(6): 102627, 2020.
Article em En | MEDLINE | ID: mdl-32682191
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Otolaringologia / Padrões de Prática Médica / Inteligência Artificial / Rinite / Aprendizado de Máquina / Otorrinolaringologistas / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Otolaringologia / Padrões de Prática Médica / Inteligência Artificial / Rinite / Aprendizado de Máquina / Otorrinolaringologistas / Aprendizado Profundo Tipo de estudo: Prognostic_studies / Systematic_reviews Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article