What Are the Ethical Issues Surrounding Extended Reality in Mental Health? A Scoping Review of the Different Perspectives
Bibliographic record
Abstract
As extended reality (XR) technologies such as virtual and augmented reality rapidly enter mental health care, ethical considerations lag behind and require urgent attention to safeguard patient safety, uphold research integrity, and guide clinical practice. This scoping review aims to map the current understanding regarding the main ethical issues arising on the use of XR in clinical psychiatry. METHODS: Searches were conducted in 5 databases and included 29 studies. Relevant excerpts discussing ethical issues were documented and then categorized. RESULTS: The analysis led to the identification of 5 core ethical challenges: (i) Balancing beneficence and non-maleficence as a question of patient safety, (ii) Altering autonomy by altering reality and information, (iii) data privacy risks and confidentiality concerns, (iv) clinical liability and regulation, and v) fostering inclusiveness and equity in XR development. Most authors have stated ethical concerns primarily for the first two topics, whereas the remaining four themes were not consistently addressed across all papers. CONCLUSIONS: There remains a great research void regarding such an important topic due the limited number of empirical studies, the lack of involvement of those living with a mental health issue in the development of these XR-based technologies, and the lack of clear clinical and ethical guidelines regarding their use. Identifying broader ethical implications of such novel technology is crucial for best mental healthcare practices.
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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 | 0.026 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".