VIRTUAL REALITY AND HEALTH SCIENCE EDUCATION: A SCIENTIFIC MAPPING WITH IMPLICATIONS FOR PUBLIC HEALTH AND DIGITAL THERAPEUTICS
Bibliographic record
Abstract
Virtual Reality (VR) is transforming health science education by enabling immersive, interactive learning environments. As global health challenges rise and digital tools proliferate, it is critical to map the evolution of VR’s application in health science education, particularly its effects on health outcomes. A bibliometric analysis was conducted using the Web of Science Core Collection from 2004–2024. We applied VOSviewer, CiteSpace, and Excel to analyze publication trends, research collaborations, thematic developments, and keyword co-occurrence. From 4,369 articles analyzed, VR-related health education publications have grown exponentially, especially after 2020. The United States, England, and Canada led in publication volume and collaboration. Keyword clustering identified five major themes: surgical simulation, immersive patient education, digital health promotion, AI-enhanced learning, and telemedicine training. Recent trends reflect a shift from technical skills training toward AI integration and personalized VR systems. VR improves learner engagement, enhances long-term health literacy, and supports behavioral change. Its integration with AI and remote delivery models facilitates scalable interventions, bridging healthcare and health science education in underserved regions. Future research should assess VR’s direct impact on clinical and public health outcomes, explore ethical and regulatory safeguards, and foster global equity in digital health science education. Keywords: virtual reality, health science education, educational technology, scientific mapping, systematic literature review
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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 | 0.033 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.067 | 0.061 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".