Use of 3D-Printed Models and Augmented Reality in Medical Student Education of Congenital Heart Disease: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Three-dimensional modalities are increasingly being used as adjuncts for medical trainees learning about complex anatomical concepts, such as congenital heart disease. Objective: This study aimed to evaluate the use of 2 such modalities, 3D-printed models, and augmented reality (AR), in improving medical students' understanding and knowledge retention of congenital heart disease when compared to traditional teaching methods. Methods: A prospective cohort pilot study was performed with 26 first-year medical students. Students were randomly assigned to receive a 30-minute teaching session using traditional slide-based lecture, 3D-printed model, or AR. Participants completed a 16-question pretest consisting of 4 basic general cardiology questions and 6 questions each regarding the anatomy and physiology of tetralogy of Fallot and hypoplastic left heart syndrome. Participants completed a posttest immediately following the teaching session, as well as a delayed posttest 3 weeks later. Results: When comparing overall and subsection posttest scores, the AR group obtained perfect immediate posttest scores at a significantly increased rate compared to the lecture and 3D model groups (6/9, 67% vs 1/8, 13% and 1/9, 11%, respectively; large effect size Cramér V=0.57; P=.02). Participants in the lecture group reported difficulty understanding cardiac anatomy and physiology using only 2D diagrams, whereas those in the 3D-printed model and AR groups almost unanimously reported improved visualization of complex cardiac defects, which enhanced their understanding. Conclusions: Due to the visuospatial benefits of 3D-printed models and AR, there is potential for use in medical education to improve students' knowledge of complex anatomical and physiological concepts. Students who received teaching using 3D-printed models or AR overwhelmingly reported improved 3D visualization of congenital cardiac defects compared to those who were taught via lecture. Additionally, AR and 3D-printed models offer practical opportunities for implementation into medical education curricula as both adjunct and stand-alone teaching modalities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".