ReviewJournal of thoracic disease2025
Bridging traditional gaps in medical training with mixed reality: insights from bronchoscopy education-a narrative review.
Review in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Benefit of mixed reality assistance on CT-based ear anatomy spatial recognition: a preliminary study.BMC medical education · 2026Article
- Initial Experience with Three-Dimensional Holographic Virtual Bronchoscopy Using Mixed Reality for Peripheral Pulmonary Lesions: A Retrospective Feasibility Study.Respiration; international review of thoracic diseases · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Medical education is undergoing a digital and intelligent revolution. Traditional bronchoscopy training methods have limitations in skill acquisition and anatomical comprehension. Mixed reality (MR), a technology that combines virtual and real environments for interactive learning, provides an innovative solution. This study reviews the current status and challenges of bronchoscopy training, highlighting the application potential of MR technology. Methods: The search strategy included literature from 2000 to 2025 in PubMed, Embase, Scopus, and China National Knowledge Infrastructure (CNKI). Eligible study types included reviews, meta-analyses, randomized controlled trials (RCTs), non-randomized trials, observational studies, case reports or series. Inclusion was restricted to studies specifically investigating MR applications in bronchoscopy procedures and medical education contexts. Key Content and Findings: MR can create immersive and interactive virtual medical training environments, equipped with anatomical reconstruction, haptic feedback, and adaptive learning functionalities. MR displays significant advantages across various medical training domains. MR technology has been successfully implemented in bronchoscopy training, demonstrating four key benefits: improved visualization of complex anatomical structures, creation of a risk-free simulated learning environment, facilitation of deliberate practice through real-time performance feedback, and enabling of remote collaborative training scenarios. In the future, MR will be deeply integrated with artificial intelligence (AI), high-fidelity haptic feedback systems (technology that simulates touch sensations like vibration or resistance), and remote collaboration technologies, offering more intelligent and personalized medical education solutions. Conclusions: MR holds considerable promise for revolutionizing bronchoscopy training. As hardware costs decrease and training systems become optimized, MR is expected to be widely adopted in medical education, potentially becoming an essential or even central component of bronchoscopy training.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.