Applications of Extended Reality (XR) in obtaining informed consent: A narrative review
Bibliographic record
Abstract
Informed consent in healthcare requires patients to have a sufficient understanding of their upcoming procedure before deciding to proceed. Unfortunately, education prior to a surgical procedure is constrained by barriers including poor health literacy, language barriers, one-sided dialogue during consultations, anxiety, and knowledge retention. Extended reality (XR), which includes virtual reality (VR), augmented reality (AR), and mixed reality (MR) has the potential to improve informed consent processes by creating an immersive, interactive, and multimodal sensory experience that supports patient education. The purpose of the study was to review the extant literature on the effectiveness of XR technology in improving patient education, a vital component of informed consent. We screened fifty-two articles and ten relevant papers from PubMed, Scopus, and Compendex, which were included in the review based on our eligibility criteria. We found that VR and AR proved effective in enhancing patient education in eight studies, and thus improving informed consent processes. MR was not utilized in the studies reviewed. The studies were conducted in several countries and positives findings were reported from a broad range of clinical settings and procedures. Though further investigation is needed, this is a promising finding that may encourage health systems to implement similar interventions prior to procedures. The review also provided an overview of the existing XR technology utilized for patient education such as a downloadable mobile application with a virtual chatbot character, and an environment designed to simulate the MRI patient’s perspective. These applications provide immersive and interactive experiences when paired with a head mounted headset such as Google VR Cardboard. The findings also revealed that XR tools are customizable and can be tailored to specific surgical procedures, which makes the potential of implementation applicable to a broader range of settings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".