Next-generation virtual and augmented reality in surgical education: a narrative review
Bibliographic record
Abstract
BACKGROUND: Virtual and Augmented Reality (VR/AR) has been used in surgery for several decades. Over the past 5-10 years, however, new technological advances, including high-resolution screens, mobile graphical processing units (mGPUs) and position-sensing technologies, have been incorporated into relatively low-cost VR and AR devices. This review focuses on the current impact of the application of these "Phase 2" VR/AR technology in surgical training. METHODS: A narrative literature review was undertaken using PubMed and Web of Science to identify comparative studies related to the impact of Phase 2 VR or AR tools on surgical training, defined in terms of the acquisition of technical surgical skills. Eleven studies on the effectiveness of VR/AR in surgical education were identified for full review. Further, the grey literature was searched for articles describing the current state of VR/AR in surgical education. A quality analysis using the Newcastle Ottawa scale showed a median score of 7 (out of a maximum achievable score of 9). RESULTS: All studies showed a positive association between the use of VR/AR in surgical training and skill acquisition in terms of improving the speed of acquisition of surgical skills, the surgeon's ability to multitask, the ability to perform a procedure accurately, hand-eye coordination and bimanual operation. The grey literature presented a common, positive theme of the benefits of VR/AR in surgical training. CONCLUSIONS: Based on the limited evidence available, VR/AR appears to have positive training benefits in improving the speed of acquisition of surgical skills. However, the significant heterogeneity in study methodology and the relative recency of wider VR/AR adoption in surgical training mean that only tentative conclusions can be drawn at this stage. Further research, ideally with large sample sizes, robust outcome measures and longer follow-up periods, is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".