Introduction

Orthopaedic surgeons were amongst the earliest and most enthusiastic adopters of additive manufacturing in medicine, due to distinct applications of technology in areas such as patient-specific instrumentation, 3D models for education and informed consent, and surgical simulation.1–4 Systematic reviews have confirmed that, in the clinical setting, 3D printing was predominantly used by surgical and procedural disciplines, in particular orthopaedic and maxillofacial surgeons, possibly due to the advantages of highly accurate, patient specific models in pre-procedural planning and rehearsal of surgical procedures.5 As 3D printers have become progressively smaller, more affordable, and accessible by orthopaedic surgeons, the American Academy of Orthopaedic Surgeons called for broader surgeon adoption of additive manufacturing into their practice to transform clinical care and education.2

Although the literature demonstrates numerous case studies of 3D printing in orthopaedics, formal structured programmes and competency frameworks to equip residents and surgeons themselves with 3D printing skills remain largely undeveloped.3,4 Even though 3D printing has been proven to be an invaluable educational tool for visualising complex fractures and providing low-cost surgical simulation, no established curriculum exists for equipping surgeons with 3D printing skills.3,6 Although medical 3D printing workshops and courses exist in the literature, they are largely aimed at the medical student level and over the course of months, which may be challenging to integrate with a packed orthopaedic surgery residency curriculum when accounting for clinical work, research, and call schedules.7–9 Given the significant time and cost savings by printing in-house, there is a clear incentive for equipping orthopaedic surgeons with the technical skills to 3D print independently.10

Consequently, while clinicians may be aware of the potential benefits of 3D printing, they lack the skills required to use it in practice. Addressing this gap is important for translating technological exposure into meaningful clinical adoption in a timely, cost-effective manner. This study therefore aims to evaluate perceptions, barriers, and readiness for clinical 3D printing adoption across the orthopaedic training continuum and to identify actionable targets for curriculum development.

Methods

Study design

Ethical approval was not required from our institution’s review board for this cross-sectional survey study. From December 2023 to June 2025, medical students and doctors in the department of orthopaedic surgery at our tertiary academic paediatric medical centre were surveyed. This article was prepared and reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist.

Eligible respondents included rotating medical students, medical officers (general junior physicians), orthopaedic registrars (senior residents) and orthopaedic consultants (attending surgeons). There were no exclusion criteria and participants were eligible to participate in the survey regardless of previous experience or training in additive manufacturing.

Survey development and data collection

Given the lack of validated instruments to assess perceptions of 3D printing in medicine, the authors developed a purpose-designed 11-item Likert-scale survey instrument. Responses were recorded on a scale of 1 (Strongly Disagree/Low) to 5 (Strongly Agree/High). The survey measured eleven distinct metrics across three domains: (1) conceptual understanding, (2) perceived barriers, and (3) clinical readiness, as detailed in Table 2. The questionnaire was designed to establish a baseline understanding of additive manufacturing, identify barriers to implementing 3D printing in clinical environments, and gauge existing readiness to utilise the technology.

Table 1.Participant demographics.
Characteristic n (%)
Age (years)
18–25 18 (26.9%)
26–33 26 (38.8%)
34–41 15 (22.4%)
42–49 4 (6.0%)
50–65 3 (4.5%)
>65 1 (1.5%)
Clinical Rank
Medical Student 15 (22.4%)
Medical Officer 27 (40.3%)
Registrar (Senior Resident) 17 (25.4%)
Consultant (Attending Surgeon) 8 (11.9%)
Total 67 (100%)

A total of 67 participants completed the survey during the study period.

Table 2.Aggregated survey results. Proportion of agreement reflects the percentage of respondents scoring ≥4 (agree or strongly agree) on the Likert scale.
Survey Item Mean ± SD Range Median Mode % Agreement
1. I understand what 3D printing is 4.00 ± 0.76 2–5 4 4 77.6%
2. I understand how 3D printing can be used in the clinical setting 3.97 ± 0.83 2–5 4 4 77.6%
3. 3D printing can be used to enhance clinical care 4.31 ± 0.63 2–5 4 4 97.0%
4. 3D printing can be used to drive research and innovation 4.42 ± 0.61 2–5 4 4 94.0%
5. 3D printing is too expensive to use in clinical practice 2.93 ± 0.80 1–5 3 3 20.9%
6. 3D printing is too time-consuming to use in clinical practice 2.78 ± 0.81 1–5 3 3 13.4%
7. 3D printing is too difficult to use in clinical practice 2.66 ± 0.86 1–5 3 3 13.4%
8. Physicians and surgeons should be trained in how to use a 3D printer 4.01 ± 0.71 3–5 4 4 76.1%
9. I am interested in using 3D printing in my clinical practice 4.01 ± 0.75 2–5 4 4 76.1%
10. I am capable of preparing my own 3D printed models 2.43 ± 1.09 1–5 2 2 14.9%
11. I plan to use 3D printing in my future clinical practice 3.69 ± 0.74 3–5 4 3 52.2%

The survey was distributed quarterly during the study period to coincide with the rotation changeover of medical students, medical officers, and registrars via an online anonymous form. All participants were informed that participation was entirely voluntary, that responses were anonymous, and that participation or non-participation would have no impact on rotation scores or clerkship evaluations. Participants were permitted to withdraw any time.

Data analysis

Statistical analysis was performed with SPSS 29 (SPSS Inc, Chicago, IL). Likert-scale data are reported as mean with standard deviation (SD), median, mode, and range. The proportion of responses ≥4 (agree or strongly agree) was calculated as a summary measure of positive endorsement for each survey item. Subgroup analyses were conducted to assess differences in perceptions of 3D printing according to clinical rank. Spearman correlation analysis was performed to examine item-level associations with personal interest (Item 9) and intent for future clinical use (Item 11). A Bonferroni-corrected significance threshold of p<0.005 was applied to account for multiple comparisons (10 tests per outcome). Given the cross-sectional study design and sample size, all analyses are descriptive and the study was not adequately powered for formal hypothesis testing.

Results

Participant Characteristics

A total of 67 participants completed the survey during the study period. Participant demographics are detailed in Table 1. By clinical rank, medical officers comprised the largest subgroup (n=27, 40.3%), followed by registrars/senior residents (n=17, 25.4%), medical students (n=15, 22.4%) and consultants/attending surgeons (n=8, 11.9%).

Overall Results

Aggregated results for all eleven survey items are presented in Table 2. Participants largely felt that 3D printing can drive research and innovation (Item 4: mean 4.42 + 0.61; 94.0% agreement) and enhance clinical care (Item 3: mean 4.31 + 0.63; 97.0% agreement). In contrast, self-rated capability to prepare 3D-printed models independently was the lowest-scoring item (Item 10: mean 2.43 + 1.09; 14.9% agreement).

The Interest-Capability Gap

There was a clear difference observed between interest in using 3D printing (mean 4.01) and self-rated capability to do so (mean 2.43), representing a 39.5% gap on the Likert scale. This pattern was seen across all clinical ranks.

Training Mandate

Regarding whether physicians and surgeons should be exposed to and trained in 3D printing (Item 8), 76.1% of all respondents scored this item ≥4. No respondent across any level of seniority rated this item below 3 (neutral).

Clinical Rank/Training Level Differences

Results by clinical rank are presented in Table 3. Registrars demonstrated the strongest overall readiness profile, reporting the highest interest (mean 4.35, 94.1% agreement), the highest intent for future clinical use (mean 4.24, 88.2% agreement) and the highest support for a training mandate (mean 4.35, 94.1% agreement). In contrast, on top of having the highest perception of barriers as described above, consultants also reported the lowest technical capability (mean 2.00, 0% agreement) and lowest interest (mean 3.62, 50% agreement). Medical students showed high belief in the clinical value of 3D printing, with 100% agreeing it can enhance care and drive research. However, they reported the lowest intent (mean 3.33, 33.3% agreement).

Table 3.Subgroup analysis by clinical rank. Values shown as mean score (% agreement, i.e. proportion scoring >4)
Survey Item Medical Student (n=15) Medical Officer (n=27) Registrar (n=17) Consultant (n=8)
1. I understand what 3D printing is 4.33 (93.3%) 3.81 (70.4%) 4.06 (76.5%) 3.88 (75.0%)
2. I understand how 3D printing can be used in the clinical setting 3.67 (73.3%) 3.89 (66.7%) 4.35 (94.1%) 4.00 (75.0%)
3. 3D printing can be used to enhance clinical care 4.40 (100.0%) 4.26 (92.6%) 4.47 (100.0%) 4.00 (75.0%)
4. 3D printing can be used to drive research and innovation 4.60 (100.0%) 4.30 (92.6%) 4.53 (100.0%) 4.25 (100.0%)
5. 3D printing is too expensive to use in clinical practice 2.47 (13.3%) 3.11 (25.9%) 2.88 (11.8%) 3.25 (37.5%)
6. 3D printing is too time-consuming to use in clinical practice 2.67 (13.3%) 2.85 (18.5%) 2.59 (5.9%) 3.12 (25.0%)
7. 3D printing is too difficult to use in clinical practice 2.40 (13.3%) 2.85 (14.8%) 2.41 (11.8%) 3.00 (12.5%)
8. Physicians and surgeons should be trained in how to use a 3D printer 3.87 (66.7%) 3.93 (74.1%) 4.35 (94.1%) 3.88 (62.5%)
9. I am interested in using 3D printing in my clinical practice 3.73 (66.7%) 4.07 (77.8%) 4.35 (94.1%) 3.62 (50.0%)
10. I am capable of preparing my own 3D printed models 2.40 (13.3%) 2.37 (14.8%) 2.76 (23.5%) 2.00 (0.0%)
11. I plan to use 3D printing in my future clinical practice 3.33 (33.3%) 3.59 (44.4%) 4.24 (88.2%) 3.50 (37.5%)

Item-Level Correlation Analysis: Drivers of Engagement

Correlation analyses were performed to identify factors associated with engagement with 3D printing, defined by personal interest (item 9) and intent for future use (item 11) as shown in Table 4. Across both outcomes, a consistent pattern emerged. The strongest positive correlates of engagement were endorsement of formal training (Item 8; interest: rho = 0.67; intent: rho = 0.56), perceived ability of 3D printing to enhance clinical care (Item 3; interest: rho = 0.58, intent: rho = 0.53), perceived innovation potential (Item 4; interest: rho = 0.51; intent: rho = 0.50), and understanding of clinical applications (Item 2; interest: rho = 0.41; intent: rho = 0.56) (all p < 0.001).

Table 4.Spearman correlation coefficients for survey items with interest (Item 9) and intent for future use (Item 11)
Survey Item Interest (Item 9) Intent for Future Use (Item 11)
Spearman rho p-value Survives Bonferroni* Spearman rho p-value Survives Bonferroni*
8. Physicians and surgeons should be trained in how to use a 3D printer. 0.67 < 0.001 Yes 0.56 < 0.001 Yes
3. 3D printing can be used to enhance clinical care. 0.58 < 0.001 Yes 0.53 < 0.001 Yes
4. 3D printing can be used to drive research and innovation. 0.51 < 0.001 Yes 0.50 < 0.001 Yes
2. I understand how 3D printing can be used in the clinical setting. 0.41 < 0.001 Yes 0.56 < 0.001 Yes
10. I am capable of preparing my own 3D printed models. 0.08 0.510 No 0.30 0.015 No
1. I understand what 3D printing is. 0.08 0.502 No 0.20 0.104 No
6. 3D printing is too time-consuming to use in clinical practice. -0.28 0.023 No -0.13 0.284 No
7. 3D printing is too difficult to use in clinical practice. -0.25 0.042 No -0.17 0.164 No
5. 3D printing is too expensive to use in clinical practice. -0.16 0.208 No -0.04 0.766 No

* Bonferroni correction applied: adjusted significance threshold p < 0.005 (10 comparisons per outcome). Bolded rho values indicate significance after correction.
Items 5–7 are barrier items; negative rho values indicate inverse associations with interest and intent. Items 9 (Interest) and 11 (Intent) are excluded as they serve as the dependent variables in this analysis.

In contrast, basic conceptual understanding of 3D printing (Item 1) was not significantly associated with either interest (rho = 0.08, p = 0.50) or intent for future use (rho = 0.20, p = 0.10).

Barrier profiling

Among the three identified barriers (cost, time, and technical difficulty), cost had the highest mean score and agreement (mean score 2.93, 20.9% agreement), which was a consistent feature across every clinical rank. Among clinical ranks, specialists/consultants were the most barrier-burdened group, with the highest mean scores and proportion of agreement across all three barriers (cost: 3.25, 37.5% agreement; time: 3.12, 25.0% agreement; technical difficulty: 3.00, 12.5% agreement).

Discussion

Primary Findings

This study demonstrates that clinicians across all training levels in orthopaedics recognise the clinical value of 3D printing, yet there remains a clear asymmetry between enthusiasm and technical readiness. The observed gap between high interest and low self-rated capability was consistent across all clinical ranks. At the same time, there was strong consensus regarding the need for formalised training, with no respondents expressing disagreement. Taken together, these findings suggest that the main barrier to adoption is not the lack of awareness, but the lack of practical training. Clinicians appear motivated to engage with 3D printing but are not adequately equipped with the skills needed to apply it in clinical settings. This disconnect likely contributes to the persistent gap between interest and real-world implementation.

Our findings align with established principles of adult learning theory, where engagement is driven by perceived clinical relevance and clinical applicability rather than abstract knowledge acquisition.11 The lack of association between basic conceptual understanding and engagement suggests that traditional didactic approaches alone may be insufficient for teaching 3D printing in medicine; instead, learning strategies that are experiential and clinically contextualised are more likely to facilitate meaningful adoption. Taken together, these support the need for structured clinical 3D printing education during and after residency, after learners have accumulated sufficient clinical experience and context to apply their knowledge accordingly.

These findings are also consistent with prior studies demonstrating that 3D printing is most valued when it is tied to concrete clinical tasks. Montgomery et al. found that 3D printed calcaneal models improved perceived fracture understanding and trainee confidence.6 More recent studies in orthopaedic trainees have also shown that patient-specific anatomical models were well received as educational tools.12,13 However, most of this work focuses on the use of pre-prepared models rather than the ability of clinicians to generate these models independently. Our results highlight an important gap, which is that while clinicians recognise the value of 3D printing, relatively few feel capable of participating in the workflow. This limitation may explain why adoption remains constrained to academic medical centres despite widespread enthusiasm.6,12,13

Emerging educational studies suggest that this gap can be addressed through structured training. Harmon et al. demonstrated that guiding learners through the complete workflow, from imaging acquisition to model production, improved confidence and reduced learner intimidation.7 Similarly, Meyer-Szary et al. showed that structured, hands-on teaching of the 3D printing processes improved participants’ technical understanding and reduced perceived difficulty.14 Although these studies were conducted primarily among medical students, they support the feasibility of teaching these 3D printing skills within a structured educational framework in the postgraduate context.

Implications for Surgical Education

Orthopaedic registrars demonstrated the highest levels of interest and intent for future use of 3D printing. This cohort is particularly well-positioned to benefit from targeted training, as they possess sufficient clinical experience to appreciate the clinical applications of 3D printing while remaining within a structured learning environment. Residency therefore represents a critical window for educational intervention. The strong support for formal training reinforces the need for deliberate integration of 3D printing into orthopaedic education.

Importantly, our findings indicate that engagement is driven more by perceived clinical relevance than by baseline conceptual knowledge. Educational strategies should therefore prioritise contextualisation and practical application. Based on these findings, we propose a clinically contextualised, three-stage curriculum framework:

  1. Clinical context and relevance

    Teaching should begin with real clinical scenarios where 3D printing adds value, such as fracture visualisation, deformity planning, and surgical simulation. Establishing clinical relevance early enhances engagement and aligns with adult learning principles.

  2. Guided technical skill development

    Learners should then be introduced to the 3D printing workflow through structured, supervised sessions, including segmentation of imaging data, creation of printable files, and basic printer operation. The aim at this stage is to achieve functional competence.

  3. Clinical integration

    Learners should subsequently apply these skills in real or simulated clinical settings, such as preparing models for preoperative planning or patient education. This stage is essential for translating technical ability into meaningful clinical application.

This framework shifts the focus of education from awareness to implementation and provides a pragmatic model for integrating 3D printing into orthopaedic training.

Perceived Barriers and Structural Constraints

Cost, time, and technical difficulty were identified as perceived barriers to 3D printing, with cost being the most prominently cited. However, there is growing evidence that challenges cost as a prohibitive factor. Parodi et al. demonstrated that the establishment of an in-house 3D printing infrastructure can be cost-effective, with initial investment offset by downstream reductions in operative time, intraoperative blood loss, and fluoroscopy usage.10 Similarly, cost-analysis studies across multiple surgical subspecialties have shown that in-house production of 3D-printed models is significantly more economical than reliance on commercial providers.15–17

These findings suggest that cost may be more appropriately conceptualised as a structural rather than individual barrier. While clinicians perceive cost as a limitation, the determinants of cost, such as infrastructure investment, workflow integration and personnel training, are largely governed at the institutional level. Notably, senior clinicians reported the greatest perceived cost burden despite being positioned to influence institutional decision-making. This highlights a key opportunity for system-level intervention to facilitate adoption.

Limitations

Several limitations should be considered. First, this was a single-centre, cross-sectional study conducted at a tertiary paediatric centre, which may limit generalisability. Second, the survey instrument was developed by the authors and was not formally validated, although this reflects the absence of any previously validated tool in this domain. Third, all data were self-reported and may not reflect actual behaviour. Fourth, the sample size was modest, causing limitations in statistical power. Fifth, the longitudinal impact of attitudes on actual 3D printing adoption cannot be assessed from this study design.

Future Directions

Future work should include the design and prospective evaluation of a structured 3D printing curriculum for residents, with assessment of both technical competency and downstream clinical outcomes. Qualitative methods should be included to provide better insights into the specific barriers and motivations that quantitative surveys cannot fully capture. Studies involving multi-centres across surgical specialties would also strengthen the generalisability of these findings.

CONCLUSIONS

This study demonstrates a consistent gap between interest in 3D printing and the technical capability to implement it in clinical practice across all levels of the orthopaedic training hierarchy. Engagement with the technology appears to be driven more by clinical relevance rather than baseline technical familiarity. Residency represents a critical and actionable window for curriculum integration, where clinical relevance and readiness for skill development are most closely aligned. Embedding structured, clinically contextualised 3D printing training within orthopaedic residency programmes may help facilitate the translation of sustained interest into meaningful clinical adoption.