Literature DB >> 30717624

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

Andrés M Bur1, Matthew Shew1, Jacob New2.   

Abstract

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.

Entities:  

Keywords:  artificial intelligence; machine learning; natural language processing; otolaryngology practice

Mesh:

Year:  2019        PMID: 30717624     DOI: 10.1177/0194599819827507

Source DB:  PubMed          Journal:  Otolaryngol Head Neck Surg        ISSN: 0194-5998            Impact factor:   3.497


  6 in total

1.  Predicting the Travel Distance of Patients to Access Healthcare Using Deep Neural Networks.

Authors:  Li-Chin Chen; Ji-Tian Sheu; Yuh-Jue Chuang; Yu Tsao
Journal:  IEEE J Transl Eng Health Med       Date:  2021-12-08       Impact factor: 3.316

2.  Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer.

Authors:  Christopher W Noel; Rinku Sutradhar; Lesley Gotlib Conn; David Forner; Wing C Chan; Rui Fu; Julie Hallet; Natalie G Coburn; Antoine Eskander
Journal:  JAMA Otolaryngol Head Neck Surg       Date:  2022-08-01       Impact factor: 8.961

Review 3.  Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences.

Authors:  Blake S Wilson; Debara L Tucci; David A Moses; Edward F Chang; Nancy M Young; Fan-Gang Zeng; Nicholas A Lesica; Andrés M Bur; Hannah Kavookjian; Caroline Mussatto; Joseph Penn; Sara Goodwin; Shannon Kraft; Guanghui Wang; Jonathan M Cohen; Geoffrey S Ginsburg; Geraldine Dawson; Howard W Francis
Journal:  J Assoc Res Otolaryngol       Date:  2022-04-20

4.  Design and Implementation of Intelligent Monitoring System for Head and Neck Surgery Care Based on Internet of Things (IoT).

Authors:  Qiuxia Liu; Sujuan Hou; Lili Wei
Journal:  J Healthc Eng       Date:  2022-02-23       Impact factor: 2.682

Review 5.  Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea.

Authors:  Hannah L Brennan; Simon D Kirby
Journal:  J Otolaryngol Head Neck Surg       Date:  2022-04-25

6.  Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings-Preliminary Data and a Pilot Study.

Authors:  Shih-Lung Chen; Shy-Chyi Chin; Chia-Ying Ho
Journal:  Diagnostics (Basel)       Date:  2022-08-12
  6 in total

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