Current Clinical and Educational Uses of Immersive Reality in Anesthesia: Narrative Review
Bibliographic record
Abstract
BACKGROUND: The concept of immersive reality (IR), an umbrella term that encompasses virtual reality, augmented reality, and mixed reality, has been established within the health care realm as a potentially valuable tool with numerous applications in both medical education and patient care. OBJECTIVE: This review aimed to introduce anesthesiologists to the emerging and rapidly evolving literature on IR, its use in anesthesia education, and its transferability into the clinical context. METHODS: A review of the relevant literature was conducted using the PubMed database from inception to July 5, 2023. Additional references were identified from the reference lists of selected papers. RESULTS: A total of 51 papers related to the use of IR in anesthesia medical education (including both technical and nontechnical skills) and 63 papers related to applications in clinical practice (eg, preprocedure planning, patient education, and pain management) were included. We present evidence supporting the use of IR in the training and clinical practice of modern anesthesiologists. CONCLUSIONS: IR is useful for a variety of applications in anesthesia medical education and has potential advantages over existing simulation approaches. Similarly, IR has demonstrated potential improvements in patient care across several clinical contexts relevant to practicing anesthesiologists. However, many applications remain in the early stages of development, and robust trials are urgently needed to confirm clinical or educational effectiveness and to assess mechanisms, educational validity, and cost-effectiveness.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".