Effectiveness of Using Augmented Reality for Training in the Medical Professions: Meta-analysis
Bibliographic record
Abstract
Background Augmented reality (AR) is an interactive technology that uses persuasive digital data and real-world surroundings to expand the user's reality, wherein objects are produced by various computer applications. It constitutes a novel advancement in medical care, education, and training. Objective The aim of this work was to assess how effective AR is in training medical students when compared to other educational methods in terms of skills, knowledge, confidence, performance time, and satisfaction. Methods We performed a meta-analysis on the effectiveness of AR in medical training that was constructed by using the Cochrane methodology. A web-based literature search was performed by using the Cochrane Library, Web of Science, PubMed, and Embase databases to find studies that recorded the effect of AR in medical training up to April 2021. The quality of the selected studies was assessed by following the Cochrane criteria for risk of bias evaluations. Results In total, 13 studies with a total of 654 participants were included in the meta-analysis. The findings showed that using AR in training can improve participants' performance time (I2=99.9%; P<.001), confidence (I2=97.7%; P=.02), and satisfaction (I2=99.8%; P=.006) more than what occurs under control conditions. Further, AR did not have any effect on the participants’ knowledge (I2=99.4%; P=.90) and skills (I2=97.5%; P=.10). The meta-regression plot shows that there has been an increase in the number of articles discussing AR over the years and that there is no publication bias in the studies used for the meta-analysis. Conclusions The findings of this work suggest that AR can effectively improve performance time, satisfaction, and confidence in medical training but is not very effective in areas such as knowledge and skill. Therefore, more AR technologies should be implemented in the field of medical training and education. However, to confirm these findings, more meticulous research with more participants is needed.
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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 | 0.023 | 0.054 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.072 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".