Integrating Virtual Reality into OSCE Training: A Novel Simulation Tool for Canadian Medical Education
Bibliographic record
Abstract
As digital tools become integral to medical practice, physicians are challenged to develop their technological competencies to optimize patient care. Technology-focused educational tools, such as virtual reality (VR) simulations, can enhance medical training by improving clinical learning and increasing proficiency with digital systems. VR has been shown to be effective in anatomy education, but its application in clinical training remains underreported.This study introduces an interactive VR simulation prototype designed to train medical students for the Objective Structured Clinical Examination (OSCE), a core component of Canadian medical education. Developed using Unity Engine and tested on the Meta Quest 2, the prototype provides a virtual clinical experience with structured, AI-driven patient encounters and feedback to strengthen core clinical competencies.Participants in the initial evaluation of the VR simulation prototype identified several key strengths associated with its use in OSCE training. Specifically, users reported the simulation's effectiveness in enhancing patient interview skills across diverse demographic groups (e.g., pediatric and multicultural populations), presenting visible manifestations of disease (e.g., gait disturbances and dermatologic abnormalities), and facilitating the recognition of auscultatory signs (e.g., atypical speech patterns and cardiorespiratory findings) within a standardized and reproducible frameworkVR-based OSCE training strengthens clinical skills and increases familiarity with digital healthcare environments. By providing standardized, immersive experiences, it enhances medical education and prepares future physicians for both traditional and technology-enhanced patient care.Clinical Relevance-Effective clinical communication and diagnostic reasoning are vital for patient outcomes. This VR tool offers a safe, standardized training environment, equipping future physicians with essential skills for real-world patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".