The role of three-dimensional printing models in medical education: a systematic review and meta-analysis of randomized controlled trials

Abstract

Objective

The objective of this study is to evaluate the role of three-dimensional printing models (3DPMs) in the medical education for undergrade students.

Method

A comprehensive search was performed across three online databases including Medline, EMBASE, CINAHL, Web of SCI, and Scopus spanning from their inception to October 30, 2024. Studies that satisfied the predefined inclusion criteria were incorporated into the analysis. Data analysis was executed utilizing RevMan 5.4.1. Subgroup analyses were conducted based on various models, and overall effects were estimated using either the fixed effects model or the random effects model. The quality of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) framework.

Results

A total of 33 studies were included in this study, involving 2716 medical undergraduates. The findings indicate that 3DPMs demonstrated significant advantages over the control group in theory test of the skeletal system with a moderate effect size (N = 646, P < 0.00001, I² = 80%, SMD = 0.56, 95% CI 0.20—0.93, Random effect model).Moreover, 3DPMs showed a moderate effect size advantage over the control group in laboratory tests with moderate effect size (N = 299, P < 0.00001, I² = 0%, SMD = 0.57, 95% CI 0.34 – 0.80, Fixed effect model). Additionally, 3DP showed advantage over the control group in total tests with small effect size (N = 832, P = 0.20, I² = 84%, SMD = 0.26, 95% CI -0.14–0.66, Random effect model).

Conclusion

3DPMs serve as a valuable adjunct to traditional teaching methodologies and have the potential to enhance both the theoretical understanding and practical laboratory skills of medical students. Nevertheless, caution must be exercised in interpreting the current findings due to variations in model types, low quality of included studies, and the limited number of studies with small sample sizes.