Research hotspots and frontier trends in the field of 3D printing in medical education from 2010 to 2025: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Three-dimensional (3D) printing is transforming medical education through the production of highly accurate anatomical models and personalised surgical training tools. Despite its growing influence, comprehensive bibliometric assessments in this domain remain scarce. This study aims to map the intellectual landscape and research trends of 3D printing in medical education from 2010 to 2025, offering evidence-based guidance for future innovation. METHODS: A systematic literature search was conducted in Web of Science Core Collection and PubMed for original articles and reviews related to 3D printing in medical education. CiteSpace was employed to construct and visualise collaboration, co-occurrence, and co-citation networks. RESULTS: The study included 302 articles from 96 institutions across 49 countries. The United States of America led in publication output, followed by China and Australia. Curtin University, the University of Toronto, and Mayo Clinic were the top three publishing institutions. The most prolific author published 11 papers, while the highest number of cited author as defined by co-citation analysis was 79. "Anatomical Sciences Education" was the most published-in and cited journal. The co-citation network analysis identified 12 thematic clusters-spanning medical modelling, anatomical education, and biomechanical testing-interconnected through pivotal high-centrality publications, illustrating the interdisciplinary expansion and evolving applications of 3D printing in medical education. Keyword analysis identified three major research hotspots: skill development and pedagogical validation, clinical surgical planning and doctor-patient communication, and emerging technologies with cross-disciplinary integration. CONCLUSION: This bibliometric analysis highlights an ongoing paradigm shift in 3D printing for medical education-from initial technical exploration toward rigorous validation of educational efficacy. Current research hotspots encompass anatomical modelling, surgical simulation, and AI/AR integration. However, persistent challenges such as limited dynamic simulation capabilities, high costs, and the absence of standardised assessment frameworks hinder progress. To realise meaningful educational transformation, strengthened interdisciplinary collaboration and technological innovation are essential to advance beyond technical demonstration toward tangible pedagogical improvement. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.153 | 0.196 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".