Virtual Reality in Medical Education during the COVID-19 Pandemic; A Systematic Review
Bibliographic record
Abstract
Abstract Introduction: With the outbreak of the COVID-19 disease and the virtualization of education, many challenges were created in the field of medical education. Many of these challenges were turned into opportunities with the help of new technologies such as virtual reality. The purpose of this research was to investigate the applications of virtual reality in medical education in the era of COVID-19. Methods: We aimed to investigate new technologies’ applications in medical education during the COVID-19 pandemic. Original English articles were browsed in online databases of PubMed, Embase, Scopus, and Web of Scienceas of November 24, 2022. Data of eligible publications were extracted following screening/ selection in two steps and applying inclusion/ exclusion criteria. This systematic review follows PRISMA checklist and Newcastle-Ottawa Scale (NOS) bias assessment tool. Results: Based on the included articles, Microsoft HoloLense2 and Meta Oculus devices were used extensively in medical training studies. In some of the studies, the results demonstrated that the use of these technologies resulted in high levels of engagement, was suitable for training purposes, and decreased the risk of medical learning practicums. Moreover, some studies observed improvement in training compared to traditional training systems. Conclusion: Extended reality use including Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR) concepts in teaching activities and practical procedures can improve the overall educational process, while also increasing engagement, motivation, and understanding of key concepts of participants, especially medical students.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".