An Overview of Clinical Applications of Virtual and Augmented Reality
Bibliographic record
Abstract
This Horizon Scan summarizes the available information regarding virtual reality (VR) and augmented reality (AR) interventions for various clinical applications. While the technologies are not new, the use of VR and AR as clinical interventions in health care is still emerging in Canadian health care systems. VR interventions have been studied in various clinical applications, including acute and chronic pain, stroke, traumatic brain injury, cerebral palsy, Parkinson disease, autism spectrum disorder, anxiety and depression, mental health in older adults, and attention-deficit/hyperactivity disorder (ADHD). Limited information on AR interventions was identified in this Horizon Scan. There is a wide range of VR and AR hardware and software available that varies in cost and complexity. Much of this hardware and software is commercially available; however, some have been developed specifically for clinical use. There are several VR interventions for various clinical indications cleared by the FDA and available in the US. Many factors should be taken into account when considering implementing a VR or AR intervention, including those related to safety, privacy, and access. It will be essential to ensure equitable access to VR and AR interventions so that their introduction does not exacerbate health inequities.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".