Bridging traditional gaps in medical training with mixed reality: insights from bronchoscopy education—a narrative review

Introduction

Background

Medical education is undergoing profound changes, with clinical skill training shifting from the traditional apprenticeship model to digital, personalized, and remote learning. This transition aims to address challenges such as limited resources, rigid training methods, and inadequate assessment systems. Bronchoscopy is a highly precise, high-risk, and technically demanding procedure, posing significant challenges in training. In this context, mixed reality (MR)—an emerging technology that combines features of virtual reality (VR) and augmented reality (AR) to overlay virtual objects onto the real world with interactive control—provides an innovative solution by constructing highly immersive and interactive learning environments (1-4). MR enables trainees to practice bronchoscopy in a zero-risk virtual setting, addressing the challenge of anatomical structure visualization and significantly improving spatial cognition and procedural accuracy. Additionally, MR supports remote teaching, real-time intelligent feedback, and data analysis, allowing trainees to practice autonomously without direct instructor supervision (5-8).

Rationale and knowledge gap

Although MR has shown promising results in enhancing bronchoscopy training, most existing studies are scattered, heterogeneous, and focused on technical feasibility rather than educational outcomes. There is a lack of comprehensive synthesis of MR’s advantages over conventional training in bronchoscopy, nor a systematic discussion on how MR can address specific limitations such as spatial cognition, procedural safety, and learner autonomy. This gap in the literature limits the translation of MR-based training into standardized curricula and evidence-based clinical practice.

Objective

This study aims to review the application of MR in bronchoscopy training, analyzing its role, core advantages, and potential limitations in anatomical visualization, procedural skill acquisition, teaching model innovation, remote collaboration, and intelligent assessment. Furthermore, we explore the broader impact of MR on medical education and discuss its future development trends. We present this article in accordance with the Narrative Review reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-565/rc).

Methods

Search strategy and selection criteria

A comprehensive systematic search was performed across four major databases: PubMed, Embase, Scopus, and China National Knowledge Infrastructure (CNKI). The detailed search strategy is presented in Table 1 and Table S1. For studies examining MR applications in bronchoscopy, we employed the search terms: (“mixed reality” AND “bronchoscopy”). Ninety-four related articles were retrieved, and nine articles were selected. To identify MR applications in medical education more broadly, we used the terms: (“mixed reality” AND (“training” OR “teaching” OR “education”)). A total of 2,753 related articles were retrieved, and 75 articles were ultimately selected.

Inclusion and exclusion criteria

We included literature published since 2000 encompassing the following study types: reviews, meta-analyses, randomized controlled trials (RCTs), non-randomized trials, observational studies, case reports or series. Exclusion criteria comprised: (I) animal or in vitro studies; (II) studies primarily focusing on VR or AR (without MR components); (III) studies focusing on the application of MR outside bronchoscopy or medical education.

Data abstraction

Two independent reviewers conducted the initial screening and selection process using the predefined criteria. For any disagreement regarding study inclusion, two blinded assessors independently re-evaluated the contested studies, with final determinations requiring at least two concordant votes.

Current status and challenges in bronchoscopy training

Apprenticeship-based training

Apprenticeship-based training is the traditional model for bronchoscopy education, where trainees progress from observation to active participation under the guidance of an experienced instructor, gradually achieving mastery of standardized endoscopic procedures and developing independent clinical decision-making abilities. Throughout the training process, instructors provide real-time supervision and feedback to ensure safe practices, and admit trainees to the next stage upon meeting specific competency benchmarks (9). However, apprenticeship-based training presents significant drawbacks. First, inexperienced trainees may increase the risk of postoperative complications due to a lack of technical proficiency. Second, they may struggle with analyzing endoscopic pathology, leading to misdiagnosis or missed diagnoses. Furthermore, variations in teaching styles and assessment standards among instructors, especially when trainees switch mentors, may hinder the consistent progression of endoscopic skills.

To enhance training effectiveness and minimize patient risk, bronchoscopy training has become increasingly diverse. Instructors utilize anatomical models, atlases, chest imaging, and endoscopic procedure videos to familiarize beginners with airway anatomy and help them develop three-dimensional bronchial mapping. Problem-based learning (PBL), case-based learning (CBL), and online education have also been incorporated into bronchoscopy training, demonstrating varying degrees of effectiveness (10).

Medical simulation training

Medical simulation training creates simulated patients and realistic clinical scenarios, allowing trainees to practice without patient risk while enabling unlimited repetitions and providing standardized feedback. Bronchoscopy simulation training primarily utilizes physical models and VR-based procedural training.

Physical simulation models

Basic simulation models allow trainees to practice repeatedly, improving the coherence and proficiency of bronchoscopy procedures. However, these models have limitations, such as non-visualized puncture paths, unrealistic anatomical structures, fragility, and inadequate haptic fidelity (11). High-fidelity respiratory system simulators, which accurately replicate internal airway structures and textures and provide realistic visuals on bronchoscope screens, offer a more immersive training experience. However, their high-cost limits accessibility.

VR-based bronchoscopy training

VR creates a high-fidelity three-dimensional virtual bronchial environment, simulating sensory interactions through visual, auditory, and limited tactile feedback. This “inside-out” perspective aligns with the actual endoscopic viewpoint, enhancing engagement and learning (12). Studies indicate that VR-based bronchoscopy training improves technical proficiency, reduces procedural errors, and lowers patient risks (9). However, VR has limitations in replicating clinical variability and complexity, making it more suitable as a supplementary rather than a replacement training modality (13). Additionally, the lack of haptic fidelity reduces the realism of procedural maneuvers, potentially impairing skill transfer to clinical practice.

Core challenges in bronchoscopy training

A major challenge in bronchoscopy training is the difficulty of anatomical visualization. The bronchial tree is highly complex and varies among individuals. Traditional teaching methods rely on two-dimensional images (CT, X-ray), anatomical atlases, or verbal descriptions, which are insufficient for developing three-dimensional spatial understanding. Moreover, the internal bronchial structures are difficult to access, requiring real-time bronchoscopy for direct visualization. Factors such as lighting, secretion obstruction, and narrow anatomical regions further increase the difficulty of recognizing small bronchial branches. Consequently, trainees often struggle with bronchial identification and navigation, relying heavily on extensive practice and experience accumulation.

Another challenge is the scarcity of practice opportunities. Bronchoscopy requires precise hand-eye coordination, meticulous control of the endoscope, and advanced spatial planning skills. Beginners often lack these abilities, increasing the risk of airway trauma, hypoxemia, and bronchospasm. To mitigate patient risks, hospitals typically impose strict restrictions on novice procedures, resulting in limited access to hands-on experience for trainees. This status creates a vicious cycle in which suboptimal learning impedes procedural opportunities, and insufficient training further obstructs the mastery of bronchoscopy skills.

Applications of MR in bronchoscopy training

MR technology is revolutionizing medical education, with applications ranging from diagnostic assistance (14) to surgical planning (15-18), intraoperative navigation (19-22), remote consultation (23,24), and medical training. In education, MR has been used for anatomy teaching (25,26), interventional procedure simulation (27,28), and surgical training (29) across multiple specialties, including urology (30), thoracic surgery (31), neurosurgery (32-34), vascular surgery (35), otolaryngology (36), and orthopedics (37).

Okachi et al. have demonstrated the potential of MR in both simulation-based training and clinical application for bronchoscopy. In their earlier study, the authors developed an autonomous MR-based training system using HoloLens 2 and Dynamics 365 Guides, which allowed trainees to interact with 3D bronchial anatomy via gesture and voice controls. The system was well received by users, showing high ratings in visualization, usability, and repeatability, and it outperformed traditional VR-based training in adaptability and interactivity (11). Building upon this, a subsequent clinical study by the same group applied MR-based 3D holographic virtual bronchoscopy to assist transbronchial biopsies. The technique enabled accurate navigation to peripheral lung lesions, improved ergonomic efficiency, and was safely implemented in two clinical cases, thus confirming its feasibility in real-world bronchoscopic procedures (38).

Talmi et al. (39) explored MR-based bronchoscopy training, focusing on its role in skill acquisition within a low-risk environment. Their system integrates self-directed learning with instructor feedback, enabling trainees to perform independent practice while receiving real-time assessments. MR first assists trainees in mastering bronchial anatomy through interactive 3D models, allowing rotation, zooming, dissection, and virtual navigation. Next, it facilitates bronchoscopy training across multiple difficulty levels, including intubation, biopsy, and lesion identification. The system automatically evaluates procedural accuracy, navigation paths, and intubation speed, providing corrective feedback for mistakes. Additionally, MR supports remote teaching and allows instructors to provide real-time guidance in a shared virtual space, enhancing educational efficiency.

Advantages and limitations: practical considerations of MR in medical education

MR technology is not just an advanced tool but a transformative force in medical education. It optimizes learning methodology, enhances training efficiency, and promotes medical education accessibility. However, like any emerging technology, MR faces several challenges despite its promising advantages.

Advantages: How MR transforms medical education

Meticulous anatomical visualization

MR overcomes traditional teaching limitations by providing intuitive, three-dimensional anatomical representations. Unlike 2D imaging, cadaveric dissection, or silicone models, MR reconstructs complex anatomical structures in high resolution. For example, it enables trainees to visualize hidden structures such as spinal nerves and cardiac valves, and supports dynamic dissection, rotation, and surgical procedure simulation. These robust functionalities facilitate the establishment of spatial relationships, substantially enhancing the capabilities of surgical planning and execution.

Risk-free training

Ethical and resource constraints have long posed challenges in medical education. Traditional training methods, including cadaveric dissection, animal experiments, and live patient practice, are limited by ethical considerations, patient safety, and cost. MR creates high-fidelity virtual training environments where trainees can repeatedly practice without risking patient harm. This safe, iterative learning approach reduces dependency on real patients and minimizes procedural risks, making medical education more accessible and ethically sound.

Efficient learning with real-time feedback

Traditional medical training relies heavily on subjective instructor assessments, which may be inconsistent and resource-intensive. MR, integrated with artificial intelligence (AI) and data analytics, automatically records procedural details such as endoscope insertion angles, instrument handling, and hand-eye coordination. Automated scoring systems provide immediate feedback, which allows trainees to identify and correct errors promptly, thereby accelerating skill acquisition. Additionally, MR systems adapt training difficulty based on individual progress, ensuring a personalized learning experience.

Remote teaching and collaboration

MR addresses the global disparity in medical education by supporting remote learning and virtual case sharing. Through real-time collaboration, experts from different locations can guide trainees via MR interfaces, breaking geographical barriers and making high-quality training accessible in resource-limited settings (40). MR also supports clinical training during public health emergencies, ensuring sustainable development of medical education (41).

Limitations: challenges and practical constraints of MR

High equipment and implementation costs

One of the primary barriers to widespread MR adoption in medical education is its high cost. The current MR devices, such as Microsoft HoloLens 2, require significant financial investment, along with specialized software and maintenance. Many institutions, especially in resource-limited regions, struggle to afford large-scale MR deployment. Even in developed countries, cost-benefit considerations slow down the integration of MR into mainstream medical curricula.

Limited haptic feedback

Medical procedures rely heavily on tactile sensations, such as the resistance encountered during tissue dissection or the feedback from catheter navigation. Current MR systems, even when integrated with haptic devices, cannot fully replicate these delicate tactile experiences. For example, in bronchoscopy training, accurate control of endoscopic force and angle is crucial. However, MR simulators face challenges in reproducing the nuanced resistance of airway tissues, which potentially impacts skill transfer to clinical scenarios.

Steep learning curve and adaptation challenges

MR technology introduces new interaction modalities, such as hand gestures and voice commands, which may be unfamiliar to medical trainees and instructors. Learning to navigate MR interfaces and manipulate 3D models requires an initial adjustment period, which increases cognitive load and learning cost. Additionally, some users may experience discomfort such as visual fatigue or motion sickness when using MR devices. To enhance usability, future MR systems should streamline user interfaces and optimize adaptation training programs.

Strengths and limitations

This narrative review provides a structured overview of the current and potential applications of MR technology in bronchoscopy education. It aims to address limitations in traditional training—such as poor anatomical visualization, limited hands-on opportunities, and lack of personalized feedback—by analyzing how MR may enhance anatomical understanding, procedural skills, and remote instruction. Based on a synthesis of recent literature, this review outlines the technical foundations of MR systems and highlights representative studies to illustrate key educational functions. It also discusses the prospective integration of MR with AI and haptic feedback systems, offering insights into future directions for digital medical education.

Nonetheless, there are certain limitations in this review. The current application of MR in bronchoscopy training remains in an early developmental stage, and available studies are scarce and concentrated. As a result, the examples cited may not fully reflect the diversity of training contexts or learner populations. Some interpretations are therefore based more on technological reasoning than on large-scale empirical evidence. Further research is needed to validate the educational effectiveness of MR through systematic implementation and quantitative evaluation.

Conclusions

MR technology is reshaping medical education by overcoming traditional training limitations, providing risk-free procedural practice and enabling personalized learning experiences. In bronchoscopy training, MR enhances anatomical visualization, improves hand-eye coordination, and offers data-driven feedback to optimize skill acquisition. Looking ahead, MR will be increasingly integrated with AI to enhance learning adaptability and procedural assessment. AI-driven MR systems will analyze user performance in real-time, offering precise recommendations for improving anatomical understanding and procedural accuracy. Additionally, AI-powered MR training platforms can simulate diverse clinical scenarios, improving trainees’ adaptability in complex clinical scenarios.

Future MR systems will also incorporate high-fidelity haptic feedback technology, such as force-feedback gloves, to enhance tactile sensation. This advancement will allow trainees to experience the resistance of airway tissues and the subtle forces involved in endoscopic maneuvers, bridging the gap between virtual training and real-world procedures. From a hardware perspective, MR devices will be more lightweight, cost-effective, and portable. Integration with 5G technology (a high-speed, low-latency wireless communication network) will further empower real-time remote teaching and surgical guidance, addressing disparities in medical education and improving training quality worldwide. MR is also expected to play a crucial role in emergency medical training and surgical simulations in remote or disaster-stricken areas.

While MR presents promising opportunities for medical education, it is not a replacement for traditional training. Medicine remains a hands-on discipline, requiring real-world patient interactions and clinical experience. Instead, MR should complement conventional teaching methods, forming a comprehensive medical training ecosystem. When personalized learning becomes the standard, MR will shift medical education from passive knowledge absorption to active experiential learning, making training more precise, flexible, and efficient. However, the ultimate success of MR in medical education depends not only on technological advancements but also on the willingness of educators, trainees, and institutions to embrace and optimize this evolving tool. By integrating MR into a well-structured training system, the future of medical education will be characterized by enhanced learning outcomes, improved patient safety, and expanded access to high-quality training resources.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-565/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-565/prf

Funding: This work was supported by Beijing Natural Science Foundation and the Beijing Economic and Technological Development Zone Innovation Joint Fund (Project Number: L248072, awarded to H.W.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-565/coif). H.W. reports that this work was supported by Beijing Natural Science Foundation and the Beijing Economic and Technological Development Zone Innovation Joint Fund (Project Number: L248072). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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