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Endoscopic Studies I: Bronchoscopy and Thoracoscopy01:30

Endoscopic Studies I: Bronchoscopy and Thoracoscopy

Endoscopy is a non-surgical medical technique used to examine a person's internal organs and vessels. This lesson will focus on two types of endoscopic studies: bronchoscopy and thoracoscopy.
Bronchoscopy
Description
Bronchoscopy is a procedure that involves direct visualization of the larynx, trachea, and bronchi for diagnostic and therapeutic purposes. A flexible fiber optic or rigid bronchoscope is used to carry out the procedure. The fiber-optic bronchoscope is more frequently used due to...

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Related Experiment Video

Updated: Jun 21, 2026

Learning Modern Laryngeal Surgery in a Dissection Laboratory
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ChatENT: Augmented Large Language Model for Expert Knowledge Retrieval in Otolaryngology-Head and Neck Surgery.

Cai Long1, Deepak Subburam2, Kayle Lowe3

  • 1Division of Otolaryngology-Head and Neck Surgery, University of Alberta, Edmonton, Alberta, Canada.

Otolaryngology--Head and Neck Surgery : Official Journal of American Academy of Otolaryngology-Head and Neck Surgery
|June 19, 2024
PubMed
Summary

A new artificial intelligence tool, ChatENT, has been developed for otolaryngology-head and neck surgery (OHNS). This specialized large language model (LLM) demonstrates improved accuracy and fewer errors compared to general LLMs in OHNS knowledge retrieval.

Keywords:
artificial intelligenceaugmented language modelingdigital healthear, nose and throatlanguage modelslarge language modelleveraged retrievalotolaryngology

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Area of Science:

  • Artificial Intelligence in Medicine
  • Otolaryngology-Head and Neck Surgery (OHNS)
  • Natural Language Processing

Background:

  • Large language models (LLMs) show promise in healthcare but have limitations like inconsistent accuracy and hallucinations.
  • A domain-specific LLM could overcome these limitations for specialized medical fields.
  • Otolaryngology-head and neck surgery (OHNS) requires accurate and reliable information retrieval.

Purpose of the Study:

  • To develop a specialized AI model for OHNS knowledge retrieval.
  • To address the limitations of general LLMs in medical applications.
  • To create an OHNS-specific question and answer platform using advanced LLM technology.

Main Methods:

  • Systematic collection of OHNS-relevant data from open-access internet sources.
  • Development of ChatENT, an OHNS-specific platform, by integrating Retrieval-Augmented Language Modeling with ChatGPT4.0.
  • Testing the model's performance on various question types, including specialty-specific examinations.

Main Results:

  • ChatENT demonstrated superior performance over ChatGPT4.0 in OHNS information analysis and interpretation.
  • Significant error reduction observed: 58.4% in Canadian Royal College OHNS questions and 26.0% in US board questions.
  • ChatENT generated fewer hallucinations and exhibited greater consistency compared to the general LLM.

Conclusions:

  • ChatENT is the first specialty-specific AI retrieval tool in medicine utilizing the latest LLM technology.
  • The model shows significant potential for medical education, patient education, and clinical decision support in OHNS.
  • ChatENT offers a more precise, safe, and user-friendly approach to AI applications in OHNS and potentially other medical fields.