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Related Experiment Video

Updated: May 19, 2026

Systematic Bronchoscopy: the Four Landmarks Approach
04:47

Systematic Bronchoscopy: the Four Landmarks Approach

Published on: June 23, 2023

Artificial Intelligence in Pediatric Bronchoscopy: Current Evidence and Future Perspectives.

Patrick Stafler1,2, Saharon Less Elazari1,2

  • 1Pulmonary Institute, Schneider Children's Medical Center of Israel, Petach Tikva, Israel.

Pediatric Pulmonology
|May 18, 2026
PubMed
Summary

Artificial intelligence (AI) shows promise in pediatric bronchoscopy for improving diagnostics and training. Further research with multi-center pediatric data is needed to overcome current limitations and integrate AI effectively.

Keywords:
artificial intelligencedeep learningmachine learningpediatric bronchoscopy

Related Experiment Videos

Last Updated: May 19, 2026

Systematic Bronchoscopy: the Four Landmarks Approach
04:47

Systematic Bronchoscopy: the Four Landmarks Approach

Published on: June 23, 2023

Area of Science:

  • Medical technology and artificial intelligence
  • Pediatric respiratory medicine

Background:

  • Artificial intelligence (AI) is advancing medical endoscopy, with established benefits in adult bronchoscopy.
  • Pediatric bronchoscopy presents unique challenges, and AI integration is an emerging area of research.

Purpose of the Study:

  • To review current evidence on artificial intelligence applications in pediatric bronchoscopy.
  • To explore potential future uses of AI in pediatric airway procedures.

Main Methods:

  • Systematic literature search of PubMed and EMBASE for AI in pediatric bronchoscopy.
  • Inclusion of relevant adult bronchoscopy studies where pediatric data were limited.
  • Focus on AI for video analysis, navigation, diagnostics, and training, noting data challenges.

Main Results:

  • Deep learning models achieve expert-level accuracy in identifying airway anatomy from bronchoscopy video.
  • AI in simulators enhances novice training in completeness, structure, and speed.
  • AI analysis of radiographs shows high accuracy for foreign body aspiration, potentially reducing bronchoscopies.

Conclusions:

  • AI in pediatric bronchoscopy is nascent, with current evidence largely based on adult practice.
  • AI holds potential to enhance diagnostic accuracy, patient safety, and training efficiency.
  • Advancement requires collaborative, multi-center pediatric data, prospective trials, and workflow integration.