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. 2025 Nov 21;11:54. doi: 10.1186/s41205-025-00304-8

Research hotspots and frontier trends in the field of 3D printing in medical education from 2010 to 2025: a bibliometric analysis

Dingyuan Jiang 1,2,✉, Nani Li 2,3, Ke Wang 1, Kui Duan 1, Jia Yang 1, Jing Zhang 1, Xueming Chen 1,✉
PMCID: PMC12639907  PMID: 41269502

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.

Keywords: 3D printing, Medical education, Bibliometric analysis, Citespace, Visual analytics

Introduction

The pace of technological progress has been remarkable, with 3D printing emerging as a pivotal technology in the medical field. Also known as additive manufacturing, this advanced process involves creating three-dimensional objects from digital models [1]. The technology works by layering materials one on top of the other, gradually building the object to its intended final form [2]. This process is guided by detailed digital design files that specify the exact sequence in which materials are deposited during construction [3]. Extensively employed in medical education for fabricating anatomical models, simulating surgical procedures, and customizing patient-specific implants, 3D printing has demonstrated efficacy in delivering cost-effective, high-fidelity educational solutions in regions with cadaver scarcity, significantly enhancing students’ mastery of complex anatomy, and enabling the development of individualized puncture simulators for patients [4–8]. These advances fully underscore its transformative potential to enhance both educational experience and patient outcomes.

While 3D printing holds significant promise for enhancing medical education, there is a noTable lack of bibliometric analysis in this area. Bibliometric analysis refers to the quantitative study of scientific literature, which helps to reveal the knowledge framework and developmental trends within specific research fields. By statistically examining data such as keywords, authors, and institutions, this analysis provides valuable insights [9, 10]. It plays a crucial role in identifying key research topics, prominent authors, and leading institutions related to 3D printing in medical education. Furthermore, it highlights the geographical distribution of research activities, helping to understand current research priorities and gaps. This analysis not only aids in predicting future research directions but also clarifies collaboration networks and knowledge sharing among various studies, ultimately promoting interdisciplinary collaboration.

The objective of this study is to perform a bibliometric analysis that reviews the research hotspots and developmental trends associated with 3D printing in medical education. This analysis aims to provide valuable insights and guidance for future research and practical applications. Such an understanding is crucial for advancing the effective use and ongoing evolution of 3D printing technology in the field of medical education.

Methods

Search strategy

On August 2, 2025, at 20:00 Beijing Time, a systematic literature search was conducted in the Science Citation Index Expanded (SCI-EXPANDED) database within the Web of Science Core Collection. The search query was structured as follows: TS=((“3D print” OR “three-dimensional print”) AND “medical education”). The timespan was restricted to publications between 2010 and 2025. Only records classified as Article or Review and published in English were included. This initial search yielded 280 publications, all of which were downloaded on the same day.

A subsequent search was performed in the PubMed database at 20:30 (Beijing Time) on the same date using the strategy: (“Education, Medical“[Mesh] AND “Printing, Three-Dimensional“[Mesh]) AND (Journal Article[pt] OR Review[pt]). The search was limited to English-language publications. This search identified 229 publications, which were also downloaded immediately.

Data exported from PubMed were then converted into a Web of Science-compatible format using CiteSpace (version 6.4.R2). The resulting dataset was merged with records obtained from Web of Science, and duplicate entries were removed algorithmically. After deduplication, a final analytic dataset of 302 unique publications was generated (Fig. 1).

Fig. 1.

Fig. 1

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Flowchart of literature search and analysis

Analysis tool

We employed CiteSpace (version 6.4.R2) for bibliometric visualization analysis [11]. The specific parameter settings were as follows: the time slice range was set from January 2010 to August 2025, with a slice duration of 1 year; node types were meticulously categorized into country, institution, author, author, cited author, reference, cited journal, and keywords; the selection criteria adopted the g-index (k = 6); link strength was measured using the cosine algorithm, while network pruning employed the pathfinder method—including dual pruning of both slice networks and merged networks [12–14].

In the collaborative network, node size corresponds to the publication output of countries/regions, institutions, or authors. Edges between nodes indicate cooperative relationships among these entities. A co-citation relationship is defined when two earlier publications are simultaneously cited by a subsequent citing article. The network analysis of these relationships constitutes the core of co-citation analysis. This methodology was extended to author and journal levels by extracting respective metadata from the cited reference publications, facilitating author co-citation analysis and journal co-citation analysis. Within the resultant co-citation networks, nodal size scales linearly with total citation frequency, serving as an indicator of intellectual influence. Edges between nodes represent a confirmed co-citation event. A chromatic scale, applied to these edges, visualizes the temporal dimension, specifically indicating the calendar year of the first observed co-citation instance for each pair.

Cluster analysis of the document co-citation network generated cluster labels algorithmically via Log-Likelihood Ratio (LLR) testing based on title words from citing articles. Prior to keyword co-occurrence analysis, search-related subject terms were excluded. Cluster exploration revealed intellectual dependencies among clusters; directed arrows illustrate the influence exerted by citing clusters on cited clusters, with the complete set (100%) of dependency pathways displayed. Clusters are ranked in descending order by size (denoted as #0, #1, …, #n) and only those containing more than five members are shown. Internal cluster consistency was assessed using the Silhouette metric, wherein values exceeding 0.7 are considered robust. The modularity of the network structure was evaluated with the Q statistic, and a value greater than 0.3 signifies significant community structure.

Betweenness centrality is an indicator that measures the importance of a node’s position in a network. The higher the betweenness centrality, the more it serves as a hub connecting other nodes. In CiteSpace visualization maps, nodes with betweenness centrality not less than 0.1 are prominently marked with purple circles [14]. Burst detection is based on temporal literature data and employs the Kleinberg’s state automaton algorithm to identify dynamic phenomena where specific terms (such as keywords, subject terms, or publications) exhibit a frequency or citation count significantly higher than historical baselines within a short period. Its core outputs include burst term intensity, start and end times, and duration cycles [14, 15]. Network density serves as a structural indicator reflecting the degree of connectivity among nodes, with standardized values ranging from 0 to 1. Lower density values are indicative of a sparser network architecture and weaker associative linkages between constituent elements.

Results

Annual and cumulative publication trends

Annual publication output in the field of 3D printing for medical education demonstrated a nonlinear growth trajectory from 2010 to 2025 (quadratic regression model: y = 1.8194x² − 2.7213x − 3.5659, R² = 0.9961). During the initial phase (2010–2015), annual publications remained low (1–6 papers). Accelerated growth commenced in 2016 (11 publications), reaching a phase peak of 34 publications in 2019. A short-term plateau with minor fluctuations occurred from 2020 to 2021 (32 to 28 publications), followed by resumption of positive growth starting in 2022 (33 publications) and culminating in a historical high of 46 publications in 2024. Cumulative publications exhibited consistent expansion throughout the study period, with a baseline value of 1 publication in 2010. A significant acceleration was observed post-2018: cumulative publications increased from 96 in 2019 to 189 in 2022 (representing 97% growth from the 2019 level), ultimately reaching 302 publications by the study’s endpoint in 2025 (Fig. 2).

Fig. 2.

Fig. 2

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The annual and cumulative publications from 2010 to 2025

Countries’/regions’ and institutions’ collaboration analysis

A total of 96 institutions from 49 countries contributed to the published articles, revealing a collaborative network that consists of 49 nodes and 64 connections, resulting in a density of 0.0544 (Fig. 3A). According to the data presented in Table 1, the United States of America led the way with the highest number of publications, totaling 95 publications. Following China and Australia ranked second and third, with 58 publications and 34 publications, respectively. The countries that ranked from fourth to tenth in publication count include United Kingdom, Canada, Germany, Brazil, Italy, France, Spain, and France. Notably, among the top ten countries, Canada, Germany, and UK exhibited the highest betweenness centrality, indicating these country plays a crucial role in connecting other country.

Fig. 3.

Fig. 3

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Country/Region (A) and institutional (B) collaboration networks

Table 1.

Top ten countries/regions and top ten institutions by publication volume

Rank Country/Region Count Centrality Institution Count Centrality
1 USA 95 0.22 Curtin University 12 0
2 China 58 0 University of Toronto 6 0.01
3 Australia 34 0.12 Mayo Clinic 6 0
4 UK 19 0.25 University of California System 5 0.01
5 Canada 19 0.73 Hospital for Sick Children (SickKids) 3 0.02
6 Germany 15 0.44 Harvard University 3 0.01
7 Brazil 13 0 Institut National de la Sante et de la Recherche Medicale (Inserm) 3 0
8 Italy 9 0.21 University of California San FranciscoRecherche Medicale (Inserm) 3 0
9 France 8 0 Air Force Medical University 3 0
10 Spain 6 0 Central South University 3 0
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The analysis of the institutional collaboration network map uncovered a total of 96 nodes and 86 links, resulting in a density of 0.0189 (Fig. 3B). Among the institutions, Curtin University led with the highest number of publications, contributing 12 publications. Following closely was the University of Toronto and Mayo Clinic with 6 publications, respectively. University of California System ranked third, with 5 publications. Nevertheless, the top ten institutions ranked by publication volume all demonstrate low betweenness centrality.

Authors’ collaboration network and co-citation analyses

Authors’ collaboration network revealed a total of 145 nodes and 249 links, resulting in a density of 0.0239 (Fig. 4A). Sun Zhonghua led in publication output with 11 publications, followed by Damon, Aaron, and William Clifton, each contributing 5 publications(Table 2).

Fig. 4.

Fig. 4

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(A) Authors’ collaboration network map and (B) authors’ co-citation network map

Table 2.

The top 10 authors by publication count and citation

Rank Author Count Cited Author Count Centrality
1 Sun, Zhonghua 11 Lim, Kah Heng Alexander 79 0.04
2 Damon, Aaron 5 McMenamin, Paul G. 52 0.02
3 Clifton, William 5 Biglino, Giovanni 48 0.36
4 Nottmeier, Eric 4 Valverde, Israel 40 0.17
5 Pichelmann, Mark 4 Loke, Yue-Hin 35 0.02
6 Adams, Justin W 4 Costello, John P. 33 0.13
7 Mogali, Sreenivasulu Reddy 3 Abouhashem Y 27 0.23
8 Chandrasekaran, Ramya 3 Preece D 25 0.23
9 Rajalingam, Preman 3 Su W 25 0.28
10 Radzi, Shairah 3 Garas M 24 0.1
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Authors’ co-citation network revealed a total of 195 nodes and 377 links, resulting in a density of 0.0199 (Fig. 4B).Lim, Kah Heng Alexander emerged as the most frequently cited author, with a total of 79 citations. Following McMenamin, Paul G. and Biglino, Giovanni were ranked second and third, with 52 and 48 publications, respectively. Notably, among the most cited authors, Biglino, Giovanni achieved the highest betweenness centrality (0.36), indicating that this author occupies a pivotal bridging position within the co-authorship network (Table 2).

Journal publication and co-citation analysis

Anatomical Sciences Education was identified as the predominant journal, publishing 26 articles, followed by BMC Medical Education (14 articles). Clinical Anatomy and World Neurosurgery each contributed 7 articles (Table 3). The journal co-citation network consisted of 172 nodes and 299 links, resulting in a network density of 0.0203 (Fig. 5). Co-citation analysis established Anatomical Sciences Education as the most influential journal, receiving 139 citations and exhibiting the highest betweenness centrality (0.24). Scientific Reports ranked second with 83 citations. Journals ranked third to tenth in citation frequency include BMC Medical Education, Journal of Surgical Education, Public Library of Science ONE, World Neurosurgery, International Journal of Computer Assisted Radiology and Surgery, Clinical Anatomy, Anatomischer Anzeiger, and 3D Printing in Medicine (Table 3).

Table 3.

Top 10 journals and cited journal

Rank Journals Count Cited Journals Count Centrality
1 Anatomical Sciences Education 26 Anatomical Sciences Education 139 0.44
2 BMC Medical Education 14 Scientific Reports 83 0.18
3 Clinical Anatomy 7 BMC Medical Education 82 0
4 World Neurosurgery 7 Journal of Surgical Education 79 0.12
5 Journal of Veterinary Medical Education 6 Public Library of Science ONE 73 0.12
6 Scientific Reports 6 World Neurosurgery 67 0.17
7 International Journal of Computer Assisted Radiology and Surgery 5 International Journal of Computer Assisted Radiology and Surgery 57 0.15
8 Surgical and Radiologic Anatomy 5 Clinical Anatomy 54 0.17
9 Academic Radiology 4 Anatomischer Anzeiger 49 0.22
10 Applied Sciences Basel 4 3D Printing in Medicine 48 0.05
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Fig. 5.

Fig. 5

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Journal co-citation network map

References co-citation analysis

The references co-citation network map generated a total of 196 nodes and 347 links, resulting in a density of 0.0182 (Fig. 6A). The study by Lim, Kah Heng Alexander et al. in Anatomical Sciences Education, entitled “Use of 3D printed models in medical education: A randomized control trial comparing 3D prints versus cadaveric materials for learning external cardiac anatomy,” demonstrated the highest citation count (n = 46) and the greatest betweenness centrality (1.090) among analyzed publications (Table 4).

Fig. 6.

Fig. 6

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References co-citation network map

Table 4.

Top 10 cited references

Rank Author Source Year Count Centrality Brief synopsis Citation
1 Lim, Kah Heng Alexander et al. Anatomical Sciences Education 2016 46 1.09 RCT: 3D-printed hearts for external anatomy teaching outperformed cadaveric materials. [16]
2 Loke, Yue-Hin et al. BMC Medical Education 2017 27 0.04 3D-printed models enhance satisfaction but not knowledge gain in teaching tetralogy of Fallot. [17]
3 Su, Wei et al. BMC Medical Education 2018 24 0.2 3D-printed heart models with VSD lesions significantly improved structural conceptualization and student engagement versus traditional teaching alone. [18]
4 Smith, Claire F.et al. Anatomical Sciences Education 2018 24 0.09 3D-printed donor-based models significantly boost undergraduate anatomy learning. [19]
22 0.09
5 Garcia, Justine et al. BMJ Simulation & Technology Enhanced Learning 2018 18 0.74 3D printing creates patient-specific anatomical models for surgical training, but current rigid materials lack tissue fidelity; future composites will achieve both anatomical and biomechanical realism. [20]
6 McMenamin, Paul G.et al. Anatomical Sciences Education 2014 18 0.01 3D printing enables rapid, accurate, and culturally neutral reproduction of prosected anatomy, offering a scalable alternative to cadaveric and plastinated specimens. [21]
7 Garas, Monique et al. Annals of Anatomy-Anatomischer Anzeiger 2018 17 0.03 3D-printed specimens outperformed wet and plastinated samples in structure identification and were rated the most usable tool. [22]
8 Chen, Shi et al. Scientific Reports 2017 14 0.01 RCT: 3D-printed color skulls modestly outperformed cadaveric skulls and atlases in novice anatomy students’ lab-based structure identification. [23]
9 Li, Zhenzhu et al. Scientific reports 2015 14 0.16 RCT: 3D-printed spinal-fracture models significantly enhanced anatomical understanding in 120 Chinese medical students, eliminating sex-based performance differences. [24]
10 Valverde, Israel et al. European journal of cardio-thoracic surgery 2017 46 1.09 Multicentre case-crossover study: 3D-printed hearts changed surgical strategy in 19/40 complex CHD cases, enabling biventricular repair in 4, and were rated highly accurate. [25]
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Based on references co-citation network analysis, 12 distinct clusters were identified with silhouette values ranging from 0.891 to 1.0 (Table 5). Cluster #0 (Medical Application, 37 articles; mean year 2007) centers on Rengier’s foundational 2010 review of medical imaging-driven modeling (1,121 citations). Conversely, Cluster #12 (Drilling Properties, 5 articles; mean year 2020) concentrates on biomechanical properties of 3D-printed cranial models (Dissanayaka, 2024). Clusters #3 (Clinical Practice) and #4 (Anatomical Education) exhibit strong co-citation linkages with #0 via Lim’s high-centrality work (2016; centrality 1.09) on patient-specific anatomical modeling. Specialized clusters (#1: Respiratory Therapy Models, #2: Tissue Regeneration, #5: Structural Heart Intervention) consistently reference Cluster #0’s methodological framework (exemplified by Ballard’s 2018 application in #5). Cluster #12 builds upon Cluster #7’s (Spinal Fracture Models; mean year 2011) mechanical testing methodology (Preece, 2013; centrality 0.73). Within Cluster #7, Rengier (2010) serves as a high-centrality interdisciplinary node bridging engineering principles and medical applications (Fig. 6B).

Table 5.

Co-citation clustering of references

Cluster-ID Size Silhouette Mean year Top terms Label
0 37 0.992 2007 medical application; review; 3d printing; systematic review; medical education medical application
1 26 0.956 2021 respiratory therapy student; measuring students perception; neck anatomy; anatomy tool; validated instrument respiratory therapy student
2 23 0.894 2015 tissue regeneration; cardiac tissue engineering; 3d printing approaches; disease severity; medical student education tissue regeneration
3 22 0.982 2018 clinical practice; patient-specific 3d-printed low-cost model; 3d modeling; congenital heart surgery; heart model clinical practice
4 16 0.965 2016 randomized controlled study; 3d printing; human anatomy; hepatic surgery; undergraduate student randomized controlled study
5 11 0.958 2015 structural heart intervention; cardiovascular surgery; current application; future challenge; cardiac model structural heart intervention
6 9 0.926 2008 education; rapid prototyping technique; medical training; modelling; application education
7 9 1 2011 spinal fracture-a; 3d printer; veterinary medicine; controlled study; anatomical teaching resource spinal fracture-a
8 8 0.979 2017 physical organ model; recent development; dynamic cervical spine model; physiology education institutional experience physical organ model
9 8 0.99 2019 digital anatomy education; scoping review; pediatric anatomy; anatomy education; 3d printing digital anatomy education
10 8 0.891 2015 liver transplantation; drug hepatotoxicity testing; liver surgeries liver regeneration; liver disease; medical 3d printing liver transplantation
12 5 1 2020 drilling properties; comparative analysis; human skull v; neurosurgical training; 3d-printed replica drilling properties
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Keyword co-occurrence analysis

After excluding the core search terms, the keyword co-occurrence map exhibits a complex network structure comprising 98 nodes and 143 links, with a network density of 0.0301 (Fig. 7A). This co-occurrence network reveals that high-frequency keywords primarily cluster around two core domains: technological applications and educational practices. Specifically, “surgical simulation” ranks first with a co-occurrence frequency of 98, followed by “3D printed models” at 94 occurrences. Betweenness centrality analysis indicates that “surgical simulation” demonstrates the highest centrality value (0.81), succeeded by “congenital heart disease” (0.74) and “education” (0.54) (Table 6).

Fig. 7.

Fig. 7

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(A) Keyword co-occurrence and (B) keyword cluster map

Table 6.

Top 20 co-occurrence keywords

Rank Keyword Count Centrality Rank Keyword Count Centrality
1 surgical simulation 98 0.51 11 augmented reality 13 0.04
2 3d printed models 94 0.81 12 graduate medical education 11 0.16
3 anatomy education 39 0.51 13 dissection 10 0.29
4 surgery 32 0.17 14 tool 9 0
5 anatomy 30 0.04 15 3d reconstruction 9 0.11
6 education 28 0.54 16 cardiac models 8 0.26
7 congenital heart disease 24 0.74 17 undergraduate medical education 8 0
8 medical students 24 0.27 18 patient 5 0
9 virtual reality 19 0.14 19 computed tomography 5 0.01
10 impact 16 0.08 20 skills 5 0
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Based on keyword co-occurrence analysis, eight distinct clusters (silhouette values 0.816–0.988) were identified, which revealed a three-tier research structure (Fig. 7B). The largest cluster #0 “Models” (size 20) focuses on 3D printed models, while cluster #8 “Multi-material 3d printing” (size 4) represents material innovation; three anatomy education subgroups (#3, #4, #5) exhibit significant thematic divergence—#3 emphasizes traditional anatomy education (“anatomy education” frequency 39), #4 centers on surgical simulation (“surgical simulation” frequency 98), and #5 targets medical student training (“medical students” frequency 24); cross-integration clusters include #1 “Congenital heart disease” (size 17) integrating cardiac pathology models with educational applications, alongside #6 “Artificial intelligence” (size 6) and #7 “Virtual reality” (size 6) demonstrating intelligent technology convergence(Table 7). The timeline visualization presents keywords within each cluster chronologically based on their year of first occurrence (Fig. 8A), thereby illustrating the developmental trajectory of 3D printing research within medical education.

Table 7.

Summary of keyword cluster analysis

Cluster-ID Size Silhouette Mean year Top terms Label
0 20 0.94 2015 models; 3d reconstruction; computed tomography; training; lumbar degeneration diseases Models
1 17 0.988 2019 congenital heart disease; 3d modeling; transesophageal echocardiography; preoperative planning; valve Congenital heart disease
2 12 0.964 2021 surgery; tissue engineering; ultrasound phantom; experimental research; medications Surgery
3 11 0.878 2018 gross anatomy education; embryology education; cadavers; image processing; fetus Gross anatomy Education
4 10 0.983 2018 gross anatomy education; surgical simulation; education; anatomy; congenital heart disease Surgical simulation
5 8 0.97 2020 gross anatomy education; undergraduate education; optometry education; clinical imaging; active learning Undergraduate education
6 6 0.894 2021 artificial intelligence; technology advances; urology; robotics; pandemic Artificial intelligence
7 6 0.816 2016 virtual reality; ventriculostomy; physical organ models; retraction; surgical applications Virtual reality
8 4 0.905 2016 multi-material 3d printing; nonlinear mechanical properties; tissue-mimicking phantoms; educational tools; metamaterials Multi-material 3d printing
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Fig. 8.

Fig. 8

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(A) Keyword clustering timeline; (B) Top 16 keywords exhibiting the strongest citation bursts

The citation bursts from 2010 to 2025 demonstrate a three-phase evolutionary trajectory: the technological foundation phase (2015–2019) was marked by modeling techniques, peaking with “3d reconstruction” (strength 3.20, 2017–2019) and “congenital heart disease” (strength 4.05, 2017–2018); the clinical translation phase (2017–2020) shifted toward surgical applications, characterized by “preoperative planning” (strength 2.16, 2017–2018) and “cardiac models” (strength 3.06, 2018–2020); and the ongoing educational validation phase (2021–2025) concentrates on pedagogical assessment, highlighted by emergent bursts in “competence” (strength 1.77, 2022–2023), “augmented reality” (strength 2.38, 2023–2025), and “validation” (strength 1.47, 2023–2025), signaling a definitive migration of research focus (Fig. 8B).

Discussion

Through a multidimensional bibliometric analysis, this study reveals the research ecosystem and developmental trajectory of 3D printing technology in medical education. Publications from 2010 to 2025 exhibit significant growth (quadratic regression model R² = 0.9961), indicating that the field has transitioned from a technological incubation phase (annual average < 6 publications) to a stage of scaled expansion. A 97% surge in output after 2019 coincided with a shift from clinical translation to educational validation, reflecting a reorientation of research emphasis from technical feasibility toward empirical verification of pedagogical value. The plateau observed in 2020–2021 mirrors the disruptive impact of the COVID-19 pandemic on hands-on educational technologies, while the strong rebound thereafter (reaching 46 publications in 2024) underscores its emergence as a core driver of innovation in medical education.

The international collaboration network exhibits distinct structural characteristics. The United States, China, and Australia collectively form the core research axis, while countries such as Canada (betweenness centrality 0.73) and Germany (0.44) facilitate global knowledge flow through their central brokerage roles. Although this collaborative architecture promotes technology diffusion, the overall institutional network density remains notably low (0.0189). High-output institutions such as Curtin University (12 publications) and the University of Toronto (6 publications) demonstrate limited deep collaboration. Notably, Mayo Clinic (6 publications), despite its focus on cardiac modelling research, has not yet established effective collaboration with materials science institutions. The absence of such interdisciplinary synergy directly impedes the development of advanced applications, including multi-material printing (research cluster #4), highlighting a critical structural deficiency within the current research ecosystem.

The academic influence network demonstrates a dual-track structure. In the author collaboration network, prolific contributors such as Sun Zhonghua (11 publications) have advanced medical education primarily through developing personalized 3D-printed models for visualizing complex anatomical structures, which significantly enhance medical students’ knowledge acquisition and long-term retention [26, 27]. In the co-citation network, high-impact authors including Lim (citation count: 79) have promoted the adoption of 3D printing technology in medical education by experimentally validating its advantages in anatomy instruction [16]. Biglino, Giovanni (betweenness centrality 0.36) serves as a critical knowledge broker, effectively bridging research clusters between anatomical education (Cluster #4) and clinical practice (Cluster #3), underscoring the emergence of randomized controlled trials as a methodological cornerstone in educational validation [28]. Journal analysis reveals Anatomical Sciences Education (citation count: 139, betweenness centrality 0.44) as a core knowledge disseminator. However, the limited engagement of engineering journals (with only 3D Printing in Medicine appearing in the top 10 by citation frequency) highlights persistent deficits in interdisciplinary depth and suggests enduring barriers to translational integration between engineering and medical sciences.

Keyword co-occurrence analysis reveals that research hotspots in 3D printing for medical education are clustered around three thematic areas: skill development and pedagogical validation, clinical surgical planning and patient communication, and technological innovation with interdisciplinary applications.

Three-dimensional (3D) printing technology demonstrates considerable promise in medical education and procedural training. In anatomical instruction, 3D-printed models significantly improve students’ spatial understanding of complex structures such as the pelvis, spine, and skull base [29, 30]. Multi-material printing techniques offer enhanced tactile feedback and improve comprehension of tissue biomechanical properties [31, 32]. Furthermore, 3D models serve as a partial substitute for traditional cadaveric specimens, mitigating ethical concerns and preservation challenges [16], proving particularly valuable in teaching delicate structures in specialties such as ophthalmology and orthopaedics [33, 34].

In surgical simulation training, 3D printing offers a low-cost, high-fidelity solution [35, 36–38]. Endoscopic surgery models produced with this technology enable practice with real instruments, markedly improving trainees’ hand–eye coordination [39, 40]。For complex procedural rehearsal, silicone models have been associated with a 22% increase in operator confidence scores [41], while transparent resin and nylon-based models for transjugular intrahepatic portosystemic shunt (TIPS) procedures provide realistic vascular access simulation and catheter manipulation training [42].

3D printing plays an increasingly critical role in clinical surgical planning and patient communication, particularly in the formulation of personalised management strategies for complex cases. Patient-specific models—such as those of vascular rings in congenital heart disease or craniofacial defects—enable enhanced precision in surgical planning [43, 44]. For instance, in cardiac surgery, 3D-printed models have facilitated the optimisation of transcatheter valve replacement pathways, significantly reducing the use of intraoperative contrast agents [45]. Furthermore, integration of digital design with metal 3D printing allows the production of mandibular reconstruction guides that achieve vessel-sparing accuracy within a 1 mm margin of error [44]. In post-oncological spinal reconstruction, customised titanium implants also ensure perfect anatomical conformity, improving surgical precision [46].

In patient communication and informed consent, 3D-printed models serve as highly effective visual aids, helping patients and their families intuitively comprehend complex conditions and treatment strategies [47]. For example, 3D-printed fetal vascular ring models enabled 90% of parents to clearly understand the malformation anatomy, leading to more engaged participation in therapeutic decision-making [43]. Similarly, models depicting degenerative lumbar conditions markedly improved patient comprehension of surgical options and satisfaction with care [48]. Multimodal visualisation technologies—such as hybrid reality (MR) models that combine 3D printing with virtual reality—offer dynamic anatomical demonstrations and further enhance the intuitiveness and interactivity of clinician–patient communication [49–53].

The integration of 3D printing across disciplines has yielded particularly notable breakthroughs in multi-material and bioprinting technologies [54–56]. Dual-material printing, combining flexible silicones with rigid polymers, enables highly biomimetic tactile properties [57], while bioprinting has successfully generated functional bladder and mini-liver constructs, offering novel pathways for transplant research [58]. Concurrently, education-research synergy is maturing: medical schools now offer dedicated 3D printing electives that train students in the full workflow from DICOM conversion to model design, thereby accelerating clinical translation [59]. Such technological convergence is not only advancing medical device development but also, through open-source databases sharing material parameters, substantially reducing the barriers to entry for multi-material printing research and innovation.

Nevertheless, the application of 3D printing in medical education continues to face substantial technical limitations and a lack of standardisation. Current models are inadequate in simulating the dynamic biomechanical properties of soft tissues—such as cardiac pulsation—often necessitating augmented reality (AR) and other complementary technologies to overcome shortcomings in dynamic representation [32, 50, 52, 60]. Although industrial-grade multi-material printers remain cost-prohibitive, open-source hardware and standardised material libraries are gradually improving the accessibility and reusability of resources [61–64]. Moreover, there is a pressing need to establish unified evaluation frameworks—including anatomical accuracy metrics and randomised controlled trials (RCTs)—to validate long-term pedagogical efficacy.

Concurrently, theoretical and ethical development lags significantly behind technological advances. Most studies focus on technical applications with insufficient integration of educational theory [27, 65–67], while ethical oversight in bioprinting remains inconsistent and interdisciplinary collaboration is often superficial [68]. To realise a paradigm shift from technology demonstration to truly transformative educational integration, future efforts must converge tissue engineering with microfluidic technologies to develop dynamic bioprinted organ models and low-cost simulators. Furthermore, multicentre collaborations and deeper integration of engineering and medical expertise will be essential to construct intelligent educational systems that incorporate real patient data, virtual interaction modules, and real-time feedback mechanisms.

Limitation

First, constrained by software and database resources, the literature screening was limited to relevant records published between 2010 and 2025 and included in the Web of Science Core Collection (Science Citation Index Expanded, SCI-EXPANDED) and PubMed. Second, to streamline the analytical process and avoid potential biases introduced by language conversion, the scope of the study was restricted to original research and review articles published in English. This screening strategy may compromise data completeness. Furthermore, as PubMed does not provide structured reference data, although the initial literature set was merged from both PubMed and Web of Science, the resulting co-citation network was constructed solely based on Web of Science data. This may lead to underrepresentation of citation relationships uniquely captured in PubMed-only records. To maintain logical coherence in the argumentation, several sources beyond the above selection criteria were incorporated in the subsequent discussion, with the aim of more clearly illustrating the development dynamics and evolutionary trends of three-dimensional printing technology in the context of medical education.

Conclusion

Based on a bibliometric analysis, this study derives the following key findings: Between 2010 and 2025, 3D printing technology in medical education exhibited marked non-linear growth, with research activity rapidly expanding from an initial phase of technological exploration to a stage focused on validation of educational efficacy. The United States, China, and Australia emerged as the core contributing countries, while nations such as Canada and Germany played pivotal academic hub roles owing to their high betweenness centrality. Nevertheless, low institutional collaboration density has impeded interdisciplinary cooperation. Research hotspots concentrate on three major themes: the use of anatomical models and surgical simulation to enhance spatial cognition and operational skills, patient-specific models for improving surgical planning accuracy and doctor–patient communication, and the integration of multi-material printing, bioprinting, along with AI/AR technologies to advance educational paradigms. Current challenges include insufficient dynamic mechanical simulation, high costs, a lack of standardized evaluation frameworks, and inadequate depth in medicine–engineering integration. Addressing these issues urgently requires interdisciplinary collaboration and technological innovation to facilitate a paradigm shift from “technology demonstration” to “educational transformation.”

Acknowledgements

Not appliable.

Author contributions

Dingyuan Jiang and Xueming Chen conceived the study and were responsible for its design. Dingyuan Jiang conducted the CiteSpace analysis, selected articles, and drafted the original manuscript. Nani Li, Ke Wang, Kui Duan, Jia Yang, and Jing Zhang subsequently revised the manuscript and provided critical editing. All authors contributed to the article and approved the version submitted for publication.

Funding

Hunan Province’s Inclusive Policy and Innovation Environment Construction Plan - Clinical Medical Technology Innovation Guidance Project (grant number 2021SK53902). Hunan Province Natural Science Foundation - Provincial and Municipal Joint Fund (grant number:2021JJ50066).

Data availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request.The database used (Web of Science and PubMed) is public and no administrative permissions were required to access the raw data.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Dingyuan Jiang, Email: jdy9180@163.com.

Xueming Chen, Email: cxm4061@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.The database used (Web of Science and PubMed) is public and no administrative permissions were required to access the raw data.

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