The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
The effectiveness of virtual reality-based technology on anatomy teaching: a meta-analysis of randomized controlled studies
Open accessAbstract
Background: Virtual reality (VR) is an innovation that permits the individual to discover and operate within three-dimensional (3D) environment to gain practical understanding. This research aimed to examine the general efficiency of VR for teaching medical anatomy. Methods: We executed a meta-analysis of randomized controlled studies of the performance of VR anatomy education. We browsed five databases from the year 1990 to 2019. Ultimately, 15 randomized controlled trials with a teaching outcome measure analysis were included. Two authors separately chose studies, extracted information, and examined the risk of bias. The primary outcomes were examination scores of the students. Secondary outcomes were the degrees of satisfaction of the students. Random-effects models were used for the pooled evaluations of scores and satisfaction degrees. Standardized mean difference (SMD) was applied to assess the systematic results. The heterogeneity was determined by I 2 statistics, and then was investigated by meta-regression and subgroup analyses. Results: In this review, we screened and included fifteen randomized controlled researches (816 students). The pooled analysis of primary outcomes showed that VR improves test scores moderately compared with other approaches (standardized mean difference [SMD] = 0.53; 95% Confidence Interval [CI] 0.09–0.97, p < 0.05; I 2 = 87.8%). The high homogeneity indicated that the studies were different from each other. Therefore, we carried out meta-regression as well as subgroup analyses using seven variables (year, country, learners, course, intervention, comparator, and duration). We found that VR improves post-intervention test score of anatomy compared with other types of teaching methods. Conclusions: The finding confirms that VR may act as an efficient way to improve the learners’ level of anatomy knowledge. Future research should assess other factors like degree of satisfaction, cost-effectiveness, and adverse reactions when evaluating the teaching effectiveness of VR in anatomy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Measuring inconsistency in meta-analyses
Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003) · BMJ · doi:10.1136/bmj.327.7414.557 · Cited by 63,139
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & the PRISMA Group* (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-151-4-200908180-00135 · Cited by 37,554
Cochrane Handbook for Systematic Reviews of Interventions: Cochrane Book Series
(2008) · doi:10.1002/9780470712184 · Cited by 25,048
Operating characteristics of a rank correlation test for publication bias
Begg, C. B., & Mazumdar, M. (1994) · Biometrics · doi:10.2307/2533446 · Cited by 16,955
Duval, S., & Tweedie, R. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.00455.x · Cited by 14,099
A Fail-Safe N for Effect Size in Meta-Analysis
Orwin, R. G. (1983) · Journal of Educational Statistics · doi:10.2307/1164923 · Cited by 2,064
The effectiveness of virtual and augmented reality in health sciences and medical anatomy
Moro, C., Štromberga, Z., Raikos, A., & Stirling, A. (2017) · Anatomical Sciences Education · doi:10.1002/ase.1696 · Cited by 1,040
Cited by
The rise of ChatGPT : Exploring its potential in medical education
Lee, H. (2024) · Anatomical Sciences Education · doi:10.1002/ase.2270 · Cited by 596
Iwanaga, J., Loukas, M., Dumont, A. S., & Tubbs, R. S. (2021) · Clinical Anatomy · doi:10.1002/ca.23655 · Cited by 328
The potential of immersive virtual reality to enhance learning: A meta-analysis
Coban, M., Bolat, Y. I., & Goksu, I. (2022) · Educational Research Review · doi:10.1016/j.edurev.2022.100452 · Cited by 354
Tang, Y. M., Chau, K. Y., Kwok, A. P. K., Zhu, T., & Ma, X. (2022) · Educational Research Review · doi:10.1016/j.edurev.2021.100429 · Cited by 281
Ryan, G. V., Callaghan, S., Rafferty, A., Higgins, M. F., Mangina, E., & McAuliffe, F. (2022) · Journal of Medical Internet Research · doi:10.2196/30082 · Cited by 242
“Anatomy was the most common topic taught with immersive technologies, which may be because of the ability of immersive technologies to enhance the understanding of complex body processes that cannot be visualized [24].”
Metadata
- Authors
- Zhao, J., Xu, X., Jiang, H., & Ding, Y.
- Venue
- BMC Medical Education
- Published
- 2020-04-25
- Volume
- 20
- Issue
- 1
- Pages
- 127-127
- ISSN
- 1472-6920 (ISSN-L)
- OpenAlex
- W3017816180
- Cited by
- 445
- References
- 45
- FWCI
- 4.95
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The effectiveness of virtual reality-based technology on anatomy teaching: a meta-analysis of randomized controlled studies. https://app.cochat.ai/papers/10.1186/s12909-020-1994-z
- The paper
- Zhao, J., Xu, X., Jiang, H., & Ding, Y. (2020). The effectiveness of virtual reality-based technology on anatomy teaching: a meta-analysis of randomized controlled studies. BMC Medical Education. https://doi.org/10.1186/s12909-020-1994-z
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.