ArticleJournal of thoracic disease2024
Artificial intelligence for automatic and objective assessment of competencies in flexible bronchoscopy.
Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in bronchoscopy: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Artificial intelligence-based image recognition in bronchoscopy: software development and randomized controlled trial for training evaluation in intensive care residents.BMC medical education · 2026Trial
- Artificial Intelligence in Pediatric Bronchoscopy: Current Evidence and Future Perspectives.Pediatric pulmonology · 2026Review
- An autonomous bronchoscopy robot controlled by artificial intelligence (BronchoBot) outperforms experienced bronchoscopists in a simulated setting.ERJ open research · 2026Article
- Article
- Comparison of ChatGPT-4o and expert evaluation in endodontic education: a cross-sectional pilot study.BMC medical education · 2025Article
- A research roadmap for AI opportunities in student assessment for medical education.BMC medical education · 2025Review
- Review
- Contemporary Concise Review 2024: New Techniques in Interventional Pulmonology.Respirology (Carlton, Vic.) · 2025Review
- Artificial intelligence improves bronchoscopy performance: a randomised crossover trial.ERJ open research · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Bronchoscopy is a challenging technical procedure, and assessment of competence currently relies on expert raters. Human rating is time consuming and prone to rater bias. The aim of this study was to evaluate if a bronchial segment identification system based on artificial intelligence (AI) could automatically, instantly, and objectively assess competencies in flexible bronchoscopy in a valid way. Methods: Participants were recruited at the Clinical Skills Zone of the European Respiratory Society Annual Conference in Milan, 9 Results: Fifty-two participants from six different continents were included. All four outcome measures of the AI correlated significantly with the experts' anatomy-ratings (Pearson's correlation coefficient, P value): DC (r=0.47, P<0.001), SP (r=0.57, P<0.001), PT (r=-0.32, P=0.02), and MIT (r=-0.55, P<0.001) and also with the experts' dexterity-ratings: DC (r=0.38, P=0.006), SP (r=0.53, P<0.001), PT (r=-0.34, P=0.014), and MIT (r=-0.47, P<0.001). Conclusions: The study provides initial validity evidence for AI-based immediate and automatic assessment of anatomical and navigational competencies in flexible bronchoscopy. SP provided stronger correlations with human experts' ratings than the traditional DC.
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