Revolutionizing medical education: Surgery takes the lead in virtual reality research
Bibliographic record
Abstract
Objectives: Advancements in technology have spurred a transformative shift in medical education, with virtual reality (VR) emerging as a powerful tool for enhancing the learning experience. This study analyses the publications of VR in medical education, focusing on differences within different medical specialties. Design: Using specific search terms, all studies published on VR in medical education listed in the Web of Science databases were included. All identified publications were analysed in order to draw comparative conclusions regarding their qualitative and quantitative scientific merit. Results: Since the first publication in 1993 and until the year 2022, there have been 1534 publications on VR in medical education. Over the years, the annual publication rate has increased almost exponentially. The studies have in total been cited 42,655 times (average 27.64 citations/publication). The leading medical field was surgery (415 publications), followed by internal medicine (117 publications), neurology (77 publications) and radiology and nuclear medicine (75 publications). Internationally, the United States (560 publications), the United Kingdom (179 publications), Canada (156 publications), Germany (139 publications) and China (100 publications) are the leading countries in this field. 37.1 % of the publications reported having received funding. Among the 100 organizations with the highest number of grants, only 8 were private companies. Conclusion: During the last 30 years, there has been a consistent rise in publications, with a notable surge observed in 2016 and 2020. The majority of the studies centered on surgical concerns. However, only a small proportion received financial support, which was particularly evident for funding originating from the private sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".