Applications and recent advances of 3D printing technology in bone diseases

Abstract

Background:

Bone diseases, including osteoporosis, arthritis, and spinal disorders, pose significant global health challenges, especially with an aging population. Traditional treatment methods often fall short in addressing the complexity and variability of these conditions. Three-dimensional (3D) printing technology has emerged as a transformative tool in orthopedics, enabling the creation of patient-specific implants, surgical guides, and anatomical models that enhance precision and personalization in treatment. Despite its potential, challenges such as regulatory barriers, high costs, and limited scalability hinder widespread clinical adoption.

Objective:

This study aims to conduct a bibliometric analysis of the research landscape surrounding the application of 3D printing in bone diseases, identifying emerging trends, influential contributors, and knowledge gaps to inform future research and clinical translation.

Methods:

This study analyzed publications on 3D printing in bone diseases from 2005 to 2024 using data from the Web of Science Core Collection. We performed co-occurrence mapping, keyword clustering, and citation analysis using CiteSpace, R Bibliometrix, and GraphPad Prism. We examined trends in research output, collaboration among countries and institutions, and thematic developments.

Results:

Between 2005 and 2024, 544 relevant publications were identified, with a rapid growth phase observed post-2016. China and the USA emerged as leading contributors, collectively accounting for over 50% of global research output. Key application areas included scaffolds for bone regeneration, tissue engineering integrating stem cell technology, and drug delivery systems. Keyword analysis highlighted “scaffolds,” “tissue engineering,” and “additive manufacturing” as central themes. Despite progress, challenges in standardization, material biocompatibility, and cost-effective manufacturing persist.

Conclusion:

3D printing is reshaping research and clinical applications in bone diseases by offering innovative solutions for bone regeneration, surgical precision, and personalized treatment. Overcoming challenges such as regulatory complexities, scalability, and interdisciplinary collaboration is critical for full clinical integration. Future research should focus on advancing bioprinting techniques, optimizing hybrid manufacturing, and leveraging artificial intelligence -driven design for enhanced patient-specific outcomes.

Highlights.

1. 3D printing can provide patient-specific solutions for bone diseases; this study maps the field’s current development.

2. Using CiteSpace and the Bibliometrix package, we mapped the temporal evolution and core themes of research hotspots.

3. Key areas include scaffolds for bone regeneration, bioprinting for tissue engineering, and drug delivery systems targeting localized treatments.

4. Closer collaboration among engineers, clinicians, materials scientists, and regulators is essential to overcoming cost, scalability, and regulatory barriers.

1. Introduction

Bone diseases represent a significant global health burden, contributing extensively to healthcare costs and patient morbidity.^[1–3] With the aging population, the prevalence of musculoskeletal conditions such as osteoporosis, arthritis, and spinal disorders is rapidly increasing, underscoring the urgent need for improved diagnostic and therapeutic strategies.^[4–7] Traditional methods often fail to address the complexity and individual variability of these conditions; however, three-dimensional (3D) printing offers a transformative solution. By enabling the customized fabrication of complex structures tailored to each patient’s unique anatomical and biomechanical needs, 3D printing marks a paradigm shift in treatment approaches.^[8]

In recent years, 3D printing technology has demonstrated transformative potential across several medical fields, including bone disease-related orthopedic applications. Patient-specific implants, such as custom joint prostheses and spinal fusion cages, are designed to match an individual’s anatomy, thereby improving alignment and integration.^[9,10] 3D printing has transformed preoperative planning and surgical simulations, enabling greater precision in complex surgeries and allowing surgeons to practice and refine techniques before operating.^[11,12] In regenerative medicine, 3D bioprinting offers groundbreaking potential by creating biocompatible scaffolds loaded with cells and growth factors to promote bone and soft tissue regeneration, especially for defects that traditional approaches cannot adequately address.^[13] Additionally, 3D printing provides a cost-effective means of producing orthopedic devices and prosthetics in resource-limited environments, thereby expanding access to quality care.^[14]

However, the global development of 3D printing for bone diseases lacks a comprehensive and integrated analysis capable of identifying future challenges. Among the major barriers are regulatory complexity, high production costs, and limited availability of suitable biomaterials. Furthermore, the absence of standardized protocols for quality control, reproducibility, and validation hinders the translation of innovative 3D-printed products from research to clinical practice.^[12,14,15] Overcoming these challenges will require the combined efforts of clinicians, engineers, materials scientists, and regulators worldwide.

This study presents a bibliometric analysis to systematically assess the current research landscape and emerging trends in the application of 3D printing technology to bone diseases. By evaluating global research output, influential publications, and thematic developments, the analysis offers a comprehensive overview and highlights key research hotspots. The insights gained are intended to inform researchers, clinicians, and policymakers seeking to apply 3D printing to unmet clinical needs and support innovation in bone disease-related research and clinical practice.

2. Methods

2.1. Data collection

Bibliometric methods enable quantitative evaluation of publication patterns and scholarly trends.^[16,17] This study retrieved literature data from the Web of Science Core Collection (WoSCC), specifically from the Science Citation Index Expanded and Social Sciences Citation Index datasets. The search covered the period from January 1, 2005, to October 31, 2024, and the final search was conducted on October 31, 2024. The query used was Topic = “bone disease” AND Topic = (“3-D print” OR “3D print” OR “3D printing” OR “3 dimensional print” OR “3-dimensional print” OR “3D printed” OR “3D print” OR “three dimensional print” OR “additive manufacturing” OR “rapid prototyping”). The scope of this study was intentionally restricted to 3D-printing research related to bone diseases rather than the broader field of orthopedic diseases. Accordingly, the term “bone disease” was used to define the target literature and to maintain consistency between the search strategy and the study objective. Only articles and review articles published in English were included.

A total of 560 records were identified from WoSCC. All retrieved records were checked for duplicate entries, and no duplicates were identified; therefore, all 560 records proceeded to relevance screening. Two reviewers screened the records for relevance according to the predefined scope of 3D printing in bone diseases. Sixteen records were excluded because they were not relevant to the predefined research scope, leaving 544 publications for the final bibliometric analysis. The 2 reviewers screened the records independently, and disagreements were resolved through discussion and consensus. The study-selection process is presented in Figure 1.

Figure 1.

Study selection flow diagram. WoSCC = Web of Science Core Collection.

Relevant details such as countries, authors, institutions, cited journals, and keywords were collected. The records were exported in TXT and BibTeX formats for subsequent analysis. To improve reproducibility, we report the database indexes, complete search query, date range, language and document-type restrictions, and export formats.

2.2. Data analysis

We used CiteSpace (version 6.4.R1), developed by Professor Chaomei Chen at Drexel University, to construct country, institution, author, and keyword co-occurrence networks and to perform keyword clustering, time-zone, and burst analyses.^[18] The analysis period was set from 2005 to 2024 with a 1-year time slice. Depending on the specific analysis, the node type was set as Country, Institution, Author, or Keyword. Nodes within each time slice were selected using the g-index with a scaling factor of k = 25. Link strength was calculated using cosine similarity, with links restricted to within individual time slices. Pruning was applied to individual time-slice networks; Pathfinder, minimum spanning tree pruning, and pruning of the merged network were not applied. Title, abstract, author keywords (DE), and Keywords Plus (ID) were included as text sources. No additional manual keyword cleaning or thesaurus-based term merging was performed before the CiteSpace analysis. Keywords were analyzed as exported from WoSCC using these selected text sources.

We created scatter plots and fitted curves using GraphPad Prism (version 10.1.2). The annual publication trend was fitted using a logarithmic regression model, expressed as y = 8006ln(x) - 60,887, where × represents time and y represents the annual number of publications. The model yielded R² = 0.5563 and was used descriptively to characterize the temporal publication trend rather than for causal inference or precise forecasting.

We also used R (version 4.4.2) with the Bibliometrix package (version 4.3.0; https://www.bibliometrix.org) to visualize Lotka’s law and Bradford’s law through the Biblioshiny interface. Keyword analyses used 1-year time slices. Where applicable, we report node and link counts, network density, keyword frequency, betweenness centrality, and cluster silhouette values.

2.3. Ethics statement

Ethics approval was not required for this study because it was a bibliometric analysis based exclusively on publicly available bibliographic data and did not involve human participants, patient-level data, human specimens, or animals.

3. Results

3.1. Overview of literature

Between 2005 and 2024, we identified 544 publications on 3D printing in bone diseases, including 411 original articles and 133 reviews. Figure 2 shows the annual publication trend and the fitted curve generated in GraphPad Prism. The fitted model yielded R² = 0.5563, indicating that it explained 55.63% of the variation in annual publication counts. We use this fit descriptively rather than as causal evidence or a precise forecast. The field remained in a nascent phase from 2005 to 2015, averaging 2.64 publications per year. Research activity then increased sharply after 2016, marking a phase of rapid development. From 2021 to 2024, annual publication output averaged 84.75 papers, reflecting a high-output yet relatively stable phase.

Figure 2.

Publication trends of 3D printing in bone disease (2005–2024). Data for 2024 reflect publications indexed up to October 31, 2024.

3.2. Analysis of countries

The country/region co-occurrence network (Fig. 3) comprised 58 nodes, 244 links, and a density of 0.1476. The network indicates an established but uneven pattern of international participation. Prominent nodes include the People’s Republic of China, the USA, and England. The top 10 countries by publication volume are highlighted in Table 1. Leading the field is the People’s Republic of China with 200 publications, followed by the USA with 119 publications, and England with 37. These 2 countries produced substantially more publications than the remaining countries/regions. In terms of betweenness centrality, the USA ranks highest at 0.32, followed by the People’s Republic of China at 0.26, and England at 0.22, underscoring the USA’s pivotal role in this domain.

Figure 3.

Country/Region contribution and collaboration network.

Table 1.

Top 10 Countries/Regions.

Rank	Country	Centrality	Yr	Count
1	PEOPLES R CHINA	0.26	2009	200
2	USA	0.32	2010	119
3	ENGLAND	0.22	2012	37
4	ITALY	0.16	2013	35
5	GERMANY	0.03	2005	31
6	SOUTH KOREA	0.03	2007	31
7	AUSTRALIA	0.01	2014	30
8	INDIA	0.10	2012	23
9	IRAN	0.05	2014	18
10	SPAIN	0.15	2017	16

3.3. Analysis of institutions

The study analyzed data from 2072 institutions, and a co-occurrence network was visualized using CiteSpace (Fig. 4). The network consisted of 297 nodes and 429 links, with a density of 0.0098, indicating a broad distribution of research among institutions but relatively sparse collaboration. Prominent nodes included the Chinese Academy of Sciences (CAS), Shanghai Jiao Tong University (SJTU), and Sichuan University. Table 2 highlights the top 10 institutions by publication volume, with 8 from China and one each from the USA and England. The CAS led with 22 publications, followed SJTU (20 publications) and Sichuan University (17 publications). In terms of centrality, the University of London ranked highest (0.14), followed by SJTU (0.12) and Harvard University (0.11).

Figure 4.

Institutional collaboration network.

Table 2.

Top 10 institutions.

Rank	Institution	Centrality	Yr	Count
1	Chinese academy of sciences	0.10	2015	22
2	Shanghai Jiao Tong university	0.12	2016	20
3	Sichuan university	0.02	2018	17
4	Harvard university	0.11	2019	13
5	University of London	0.14	2012	12
6	Jilin university	0.01	2017	12
7	Peking university	0.02	2017	11
8	Shanghai institute of ceramics	0.00	2016	8
9	University of Chinese academy of sciences	0.01	2018	8
10	Chinese academy of sciences	0.10	2015	8

3.4. Analysis of authors and cited literature

In the field of 3D printing for bone disease applications, a total of 3448 authors have contributed, with the top 10 most prolific authors highlighted in Table 3. The leading author, Wang Jincheng, published 7 studies, indicating that individual author productivity in this area is relatively low. Additionally, the initial publications from these top authors occurred relatively recently, suggesting significant untapped research potential in the field. Lotka’s Law, visualized using the Biblioshiny (Fig. 5A), under scores the characteristic distribution where a small number of researchers contribute to the majority of studies. The CiteSpace author co-occurrence network (Fig. 5B) contained 501 nodes, 971 links, and a density of 0.0078, indicating sparse collaboration among researchers. Table 4 presents the top 10 most-cited publications, with Xu NF’s study, “Reconstruction of the Upper Cervical Spine Using a Personalized 3D-Printed Vertebral Body in an Adolescent With Ewing Sarcoma,” leading with 17 citations. Meanwhile, Mandrycky C’s “3D bioprinting for engineering complex tissues” holds a relatively central position with a betweenness centrality of 0.14, highlighting its influential role within the domain. Some influential papers also presented a cautious view of the future of 3D printing and discussed unresolved challenges, including “3D bioprinting for biomedical devices and tissue engineering: A review of recent trends and advanced” with centrality of 0.04.

Table 3.

Top 10 authors.

Rank	Author	Institution	Country	Yr	Count
1	Wang, Jincheng	Jilin university	China	2019	7
2	Wu, Chengtie	Wuhan university of technology	China	2016	6
3	Zhang, Li	Jin Yin tan Hosp	China	2021	4
4	Dai, Kerong	Shanghai Jiao Tong univ	China	2021	4
5	Liu, He	China Univ Petro East China	USA	2019	4
6	Zhang, Meng	Huazhong university of science & technology	China	2022	3
7	Beltrami, Giovanni	Azienda Ospedaliera Universitaria (AOU) MEYER	Italy	2018	3
8	Chang, Jiang	University of Chinese academy of sciences	China	2016	3
9	Liu, Zhongjun	Peking university	China	2020	2
10	Feng, Ganjun	Sichuan university	China	2021	2

Figure 5.

Author Analysis: (A). Lotka’s Law View; (B). Author Collaboration Network.

Table 4.

Top 10 cited literature.

Rank	Literature	Centrality	Yr	Cited-Count
1	Reconstruction of the upper cervical spine using a personalized 3D-printed vertebral body in an adolescent with Ewing Sarcoma	0.05	2016	17
2	Materials design for bone-tissue engineering	0.03	2020	14
3	3D printing of bone tissue engineering scaffolds	0.02	2020	14
4	A 3D bioprinting system to produce human-scale tissue constructs with structural integrity	0.07	2016	13
5	3D bioactive composite scaffolds for bone tissue engineering	0.02	2018	13
6	Progress in 3D bioprinting technology for tissue/organ regenerative engineering	0.02	2020	12
7	3D bioprinting of tissues and organs	0.04	2014	12
8	3D bioprinting for biomedical devices and tissue engineering: A review of recent trends and advances	0.04	2018	11
9	Vascularized 3D printed scaffolds for promoting bone regeneration	0.01	2019	11
10	3D bioprinting for engineering complex tissues	0.14	2016	11

3.5. Analysis of journals

We analyzed 274 journals using an overlay map to illustrate knowledge flows between cited and citing literature in the field of 3D printing for bone diseases. The generated overlay map (Fig. 6A) displays the disciplinary distribution of cited literature on the left and citing literature on the right. Wave-like connections between the 2 halves highlight the relationship between research foundations and current advances. Research on 3D printing for bone diseases primarily spans molecular biology, immunology, physics, materials science, and chemistry. Citation patterns reveal clusters concentrated in molecular biology, genetics, chemistry, materials, and physics journals. In the left classification units, molecular biology, immunology, and physics, materials, chemistry exhibit 2 outbound citation pathways, connecting to clusters in molecular biology, genetics (Z = 2.39, 4.23) and chemistry, materials, physics (Z = 2.05, 7.06), respectively.

Figure 6.

Journal Analysis: (A). Dual Overlay Map of Journals; (B). Bradford’s Law View.

According to Bradford’s law, the most relevant publications are concentrated in a relatively small subset of journals (Fig. 6B). Table 5 highlights the top 10 journals by publication volume, which collectively account for only 17.46% of the total publications indicating a relatively dispersed literature distribution. These journals primarily cover biology, materials, healthcare, and surgery, reflecting the multidimensional and interdisciplinary nature of research in this field. The average impact factor of the top 10 journals is 6.6, with Advanced Functional Materials ranking the highest at 18.5, underscoring the growing attention of high-impact journals to this domain. These findings suggest an increasingly diverse and comprehensive exploration of 3D printing applications in bone disease research, with scholars contributing perspectives from various disciplines.

Table 5.

Top 10 journals.

Rank	Journals	Count	IF
1	FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY	15	4.3
2	ACTA BIOMATERIALIA	14	9.4
3	BIOMATERIALS	11	12.8
4	ADVANCED FUNCTIONAL MATERIALS	8	18.5
5	INTERNATIONAL JOURNAL OF BIOPRINTING	8	6.8
6	JOURNAL OF CRANIOFACIAL SURGERY	8	1.0
7	MATERIALS	8	3.1
8	ORTHOPAEDIC SURGERY	8	1.8
9	POLYMERS	8	4.7
10	ANNALS OF TRANSLATIONAL MEDICINE	7	3.6

IF (Impact Factor) statistics are as of November 12, 2024.

3.6. Analysis of keywords and hotspots

We generated a co-occurrence map of keywords (Fig. 7) using 1-year time slices, yielding 439 nodes, 2426 connections, and a density of 0.0252. In the context of bone disease, 5 hotspot terms were identified: “scaffolds,” “tissue engineering,” “additive manufacturing,” “bone regeneration,” and “drug delivery.” Table 6 lists the top 20 keywords by frequency, with these 5 terms appearing 69, 54, 46, 41, and 28 times, respectively. High-frequency topics were dominated by “scaffolds,” “tissue engineering,” “additive manufacturing,” and “bone regeneration.” Among these 5 hotspot terms, “scaffolds” and “drug delivery” had the highest centralities (0.10 and 0.09, respectively), indicating their bridging roles within the keyword network. We analyzed the temporal evolution of keywords in the field of 3D printing for bone diseases using time zones as analytical nodes, producing a time-series visualization (Fig. 8). The time-zone visualization showed that additive manufacturing, 3D bioprinting, and mechanical properties have attracted increasing attention in recent years.

Figure 7.

Keyword co-occurrence network.

Table 6.

Top 20 keywords.

Rank	Keywords	Centrality	Yr	Count
1	3d printing	0.14	2005	155
2	Bone	0.11	2009	75
3	Scaffolds	0.1	2009	69
4	Mesenchymal stem cells	0.17	2011	60
5	In vitro	0.18	2006	58
6	Tissue engineering	0.02	2010	54
7	Mechanical property	0.05	2017	52
8	Additive manufacturing	0.04	2012	46
9	Fabrication	0.05	2005	45
10	Stem cells	0.05	2010	44
11	Bone regeneration	0.04	2016	41
12	Disease	0.03	2013	39
13	Differentiation	0.16	2010	34
14	Design	0.11	2005	30
15	Drug delivery	0.09	2016	28
16	Biomaterials	0.02	2014	28
17	Reconstruction	0.13	2017	28
18	Bone tissue engineering	0.03	2005	28
19	Osteogenic differentiation	0.02	2017	27
20	Composite scaffolds	0.07	2010	26

Figure 8.

Keyword time zone view.

Clustering analysis of keywords revealed 9 distinct research clusters related to 3D printing in bone disorders over the past 2 decades (Fig. 9): #0 computer-aided design, #1 surgery, #2 stem cells, #3 tissue engineering, #4 3D printing, #5 focused ultrasound, #6 bone tissue engineering, #7 in vitro, and #8 tandem repeat proteins. Table 7 summarizes cluster-specific details. The themes highlight applications of 3D printing in areas such as bone tissue engineering, surgical interventions, and drug delivery. Each cluster achieved an average silhouette value above 0.7, indicating robust clustering and relevance to the research focus. The average publication year of keywords within each cluster reflects the temporal trend of research activity.

Figure 9.

Keyword clustering analysis.

Table 7.

Keywords content.

Cluster	Keywords content	Number of nodes	Average profile value
0	Computer-aided design; tissue engineering; 3d printing technology; chordoma; patient education	66	0.749
1	Surgery; 3-dimensional printing; fibula free flap; bones; case report	61	0.8
2	Stem cells; growth factor; 3d-print; osteochondral defect; rheumatoid arthritis	59	0.76
3	Tissue engineering; polylactic acid; biomaterials; nanocomposite; 3-d printing	58	0.755
4	3d printing; drug delivery; bone regeneration; 3d bioprinting; 3d scaffolds	53	0.617
5	Focused ultrasound; bone infection; carbon nanotube; repair strategies; ultrasonic imaging	46	0.815
6	Bone tissue engineering; composite; nanotechnology; calcium phosphate; bone substitute	45	0.668
7	In vitro; mesenchymal stem cells; osteomyelitis; mechanical reinforcement; bone replacement	30	0.843
8	Tandem repeat proteins; trypanosoma cruzi; direct agglutination test; strip test; rk39	12	0.999

3.7. Analysis of research evolution

We identified the 15 keywords with the strongest citation bursts in this field (Fig. 10). During 2005 to 2015, the field was in its infancy, with a primary focus on rapid prototyping (burst strength: 4.41) and design (2.07). From 2017 to 2021, research and applications in bone-related 3D printing concentrated on keywords such as reconstruction (3.09), management (2.25), 3D (2.59), defects (3.93), mechanical property (3.05), mandibular reconstruction (2.57), articular cartilage (2.26), size (2.14), resection (3.43), outcome (2.64), surgery (2.43), and head (2.31). More recently, from 2022 to 2024, osteogenesis became a prominent focus, with a burst strength of 2.21.

Figure 10.

Keyword burst view.

4. Discussion

4.1. General information

Our bibliometric results show rapid growth in 3D printing research related to bone diseases. Publication activity remained limited during the exploratory period from 2005 to 2015, then expanded sharply after 2016 as 3D printing gained recognition as an important tool in bone disease-related research and clinical applications. From 2021 to 2024, annual output averaged more than 80 publications, suggesting that the field had entered a high-output phase.

China and the USA were the leading contributors. China produced the most publications, whereas the USA had the highest betweenness centrality, suggesting a stronger bridging role within the international collaboration network. Institutions such as the CAS and SJTU have played an important role in the field of 3D printing for bone diseases by publishing a large number of studies. Nevertheless, the sparse collaboration networks indicate substantial scope for stronger cross-national partnerships.

The journals represented in this study span materials science, biology, immunology, genetics, physics, and chemistry, highlighting the field’s interdisciplinary character. Stronger integration across these disciplines may accelerate the development and clinical translation of 3D printing for bone diseases. Meanwhile, high-impact journals such as Advanced Functional Materials and Biomaterials have published work in this area. Although some highly cited studies emphasize innovations in bone regeneration and bioactive materials, others adopt a more cautious perspective and identify unresolved challenges. Addressing these challenges by strengthening international ties, interdisciplinary collaborations, and strategic innovations is essential for translating research results into clinical practice.

4.2. Hotspots in 3D printing for bone diseases

Thematic analysis of 3D printing applications in bone diseases reveals scaffolds, tissue engineering, and drug delivery systems as central areas of focus. Scaffolds have emerged as a key research priority due to their ability to replicate the extracellular matrix, providing structural support and fostering cellular adhesion, proliferation, and differentiation.^[19] These innovations are especially relevant to complex bone defects that traditional surgical methods cannot adequately repair.^[20] Advanced scaffolds loaded with bioactive molecules and growth factors have demonstrated enhanced osteointegration and bone regeneration potential.^[21,22]

Tissue engineering, in conjunction with 3D printing, has provided a platform for the development of complex constructs incorporating stem cells and biomaterials. Bioprinted scaffolds seeded with mesenchymal stem cells have shown promise in preclinical studies for promoting osteogenesis and angiogenesis, offering viable solutions for nonunion fractures and large bone defects.^[23–26] The therapeutic application of mesenchymal stem cell-derived exosomes in bone and joint-related diseases, such as osteoarthritis, holds significant promise, particularly when integrated with advancements in 3D printing technologies.^[27] Drug delivery systems represent a highly favored area of investigation among researchers. 3D printing has enabled the development of customizable implants capable of releasing therapeutic agents in a controlled manner, targeting specific sites with high precision.^[28–30] These systems not only improve local therapeutic efficacy but also minimize systemic side effects. For example, localized metformin delivery through a 3D-printed gelatin methacryloyl -nanoclay hydrogel scaffold illustrates this site-specific strategy.^[31]

4.3. Emerging frontiers in 3D printing for bone diseases

The frontiers of 3D printing in bone disease-related research are defined by advancements in 3D bioprinting and hybrid manufacturing. Bioprinting technologies have evolved to enable the deposition of cells and biomaterials with precision, facilitating the creation of vascularized bone constructs. This progress holds the potential to address critical gaps in bone regeneration, particularly in areas where vascularization is essential for tissue viability and integration.^[32–34]

Hybrid manufacturing, which combines traditional machining and 3D printing, has enabled the production of multi-material constructs with superior mechanical and biological properties.^[35–37] This approach leverages the strengths of both methods to develop implants tailored to specific clinical needs. Additionally, artificial intelligence and machine learning are transforming design processes by optimizing structural configurations, predicting mechanical behaviors, and enhancing manufacturing efficiency.^[38,39] These technologies also aid in analyzing patient data, generating customized treatment plans, and improving patient-specific outcomes.^[40]

Furthermore, the integration of sensors into 3D-printed constructs marks a significant innovation. These “smart” implants can monitor real-time healing processes, enabling clinicians to make timely interventions.^[41,42] Advances in biodegradable and bioresorbable biomaterials also expand the scope of 3D printing, offering sustainable and biocompatible solutions for various bone disease-related applications.^[43,44]

Despite substantial progress, limited international collaboration and unresolved issues in the reproducibility and quality control of clinically used printed structures continue to constrain the field.^[45] Interdisciplinary collaboration is essential to overcoming these challenges. Bringing together clinicians, engineers, materials scientists, and regulatory agencies can facilitate the development of robust frameworks for quality control and regulatory compliance.^[46,47] Additionally, training programs for clinicians and technicians will help bridge knowledge gaps, ensuring the effective and widespread adoption of 3D printing in clinical practice.

5. Conclusion

3D printing is reshaping research and clinical applications in bone diseases by offering new approaches to bone regeneration, personalized treatment, and defect repair. Its ability to produce patient-specific, functional, and biocompatible constructs marks a paradigm shift in managing bone-related disorders. While significant progress has been made, addressing challenges such as regulatory barriers, cost limitations, and scalability issues will be crucial for its full integration into clinical workflows. Continued interdisciplinary collaboration and innovation in materials, design, and manufacturing may support safer and broader clinical integration of patient-specific 3D printing in bone disease-related care.

Author contributions

Investigation: Yin-Yao Gou, Ren-Song Li, Xin Zhao, Lian-Shan Qi.

Software: Yin-Yao Gou, Ren-Song Li, Lian-Shan Qi.

Validation: Yin-Yao Gou, Li-Kun Wei, Zhi-Gang Xiao.

Visualization: Yin-Yao Gou, Li-Kun Wei, Zhi-Gang Xiao.

Data curation: Li-Kun Wei, Hao Liu.

Project administration: Li-Kun Wei, Ren-Song Li, Xin Zhao.

Formal analysis: Ren-Song Li, Xin Zhao, Hao Liu.

Supervision: Zhi-Gang Xiao.

Conceptualization: Xin Zhao, Hao Liu.

Resources: Xin Zhao, Lian-Shan Qi.

Funding acquisition: Hao Liu.

Writing – original draft: Yin-Yao Gou, Li-Kun Wei.

Writing – review & editing: Yin-Yao Gou, Li-Kun Wei.