A systematic literature search was conducted using PubMed, the Cochrane Library, and the Web of Science. The search strategy included the terms: “(orthopedic surgery or orthopedic disease) and (traumatological surgery or trauma or traumatic fracture) and (image processing or designing or computer-aided manufacturing or CAM or computer-aided design or CAD or patient-specific) and (3D printing or three-dimensional printing or additive manufacturing or rapid prototyping or 3D model or model for surgery planning) and (quality or quality indicator) and (process management)”.

Eligible publications were limited to peer-reviewed articles, classic articles, practice guidelines, systematic reviews, and reviews, published between January 1, 2000, and February 2024, in English, German, or Spanish. English was prioritized due to its global relevance in scientific communication, while German and Spanish were included for their regional clinical relevance and the review team’s language proficiency. Non-peer-reviewed articles, case reports, commentaries, and conference abstracts were excluded.

Additional relevant records were identified through manual searches. All references were imported into Endnote X9.1, and duplicates were removed. Following the identification of 18 additional records and the removal of 15 duplicates, 1,353 records were screened. These were imported into Rayyan QCRI (Qatar Computing Research Institute) (https://rayyan.qcri.org), a web-based tool that facilitates blind, independent screening of titles and abstracts, enabling collaborative decision-making and conflict resolution among reviewers [17].

Screening and evaluation followed the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) 2015 checklist to ensure transparency, replicability, and completeness [18–20]. Titles and abstracts were assessed using the PICO (population, intervention, comparison, outcome) framework, a standard approach in evidence-based medicine. An overview of the search process is provided in Table 1.

A total of 45 full texts were included for further analysis using Voyant Tools (https://voyant-tools.org/), an open-source text-mining software that enables the visualization and statistical evaluation of textual data [21]. Documents were uploaded from local files, and word frequency analyses and word clouds were generated. For enhanced visualization of high-frequency terms, the Cyrrus tool was additionally employed to display central terms in a consolidated format [21, 22].

To explore deeper linguistic and semantic structures, the following Voyant Tools modules were applied:

(1)

Collocates Graph Tool—identifies co-occurrence patterns;

A relative frequency threshold of 0.0125 was defined for key terms to be included in the qualitative synthesis regarding quality-related themes.

Based on the findings of the literature search on quality approaches and standards in 3D printing for orthopedic and traumatological care, a model of internal and external 3D printing processes was developed using ADONIS (https://www.boc-group.com/de/adonis/). The modeling followed the business process model and notation (BPMN) standard, developed by the Object Management Group and listed under ISO (International Organization for Standardization) 19510 since 2013 [25]. A detailed overview of the underlying process documentation is provided in the supplementary material (Table S1), and the complete visual representation of the workflow is shown in Figure S1 (supplementary material).

BPMN provides a unified graphical language to visualize, document, and analyze complex workflows using standardized symbols and diagrams. This facilitates transparent communication of processes and supports alignment with institutional objectives.

A total of 1,305 articles and 48 book chapters were identified. Records were excluded due to their irrelevance to the predefined PICO questions, while 284 remaining full texts were checked for suitability using the PRISMA-P 2020 checklist. The flowchart of the systematic review is shown in Figure 1.

The analysis of trends in content-related aspects identified five key issues for further content analysis. These issues are outlined in the following paragraphs.

Each 3D-printed anatomical model was identified and documented by assigning a unique identifier to each model for traceability purposes. This could be done directly via patient information such as surname or clinic number or by generating a unique identifier linked to the information. Any patient information was protected under the Health Insurance Portability and Accountability Act (HIPAA), similar to other areas of medical practice. Additional labeling, such as left or right or mirror image anatomy, was required and printed on the anatomical models using a CAD program. Long-term storage: the segmentation, STL files, and photos of each model were stored in a dedicated repository for long-term storage. In addition, an image of each model was archived in the patient’s medical record. The STL files can be used at any time to reprint the model without re-segmenting the images or redesigning the parts. All files were stored on secure, high-capacity servers with daily backups [34]. Beyond data protection, ethical considerations such as informed consent for the use of imaging data in model generation and reuse are essential, particularly when used for training or research purposes [2]. Institutions are encouraged to establish governance frameworks that uphold transparency and patient autonomy in the handling of 3D-printed anatomical data [36].

Professionals working in 3D printing were required to maintain a high level of training and competence, and the use of FDA (Food and Drug Administration)-approved software and hardware was recommended [36]. Similarly, Jin et al. [31] emphasized that training and competence were essential for 3D printing professionals. It was recommended that FDA-approved software and hardware be used when necessary to ensure safety and efficacy. Education and training of healthcare 3D printing talent was crucial in meeting the increasing demand for expertise. Universities and vocational schools should establish relevant degree programs to cultivate workforce and research talents. Large manufacturing or technology companies could invest equipment resources in schools to compensate for the lack of training in this field. Another strategy was to train existing talents in related fields, such as doctors in design engineering or biomedical engineers in anatomy and surgical procedures, to use them as coordinators in the development process of 3D-printed medical products. Jin et al. [31] called for more policy measures to ensure the training, employment, and skills enhancement of relevant talents and to promote the development of this field.

Medical 3D-printed products are regulated by various authorities worldwide, including the FDA (USA), EMA (European Medicines Agency) [EU (European Union)], NMPA (National Medical Products Administration) (China), and MFDS (Ministry of Food and Drug Safety) (South Korea). These bodies define which products are medical devices, their approval processes, and the requirements to enter the market.

In the USA, the FDA has advanced regulations since a 2014 workshop titled ‘Additive Manufacturing of Medical Devices’, addressing quality and safety aspects of 3D printing, including bioprinting and metal implants. The 2016 guidance ‘Technical Considerations for Additively Manufactured Medical Devices’ outlines the technical factors throughout the additive manufacturing process, from design to post-processing and testing. The subsequent document ‘Additively Manufactured Medical Devices—The FDA Perspective’ highlights ongoing quality and safety challenges [36]. Most 3D-printed implants fall under Class III, requiring compliance with strict regulatory standards regardless of manufacturing technique [37].

The EU regulates 3D-printed medical devices under Regulation (EU) 2017/745, demanding stringent controls across design, production, marketing, and distribution to ensure patient safety [4]. The 2009 Guideline for Manufacturers of Customized Medical Devices describes regulatory steps for custom devices, while the IMDRF’s (International Medical Device Regulators Forum’s) 2018 Guideline on Terminology and Classification for Personalized Medical Devices fosters international harmonization.

In China, 3D-printed medical devices have been part of national development strategies since 2015. The NMPA issued technical guidelines for the evaluation and registration of customized devices and adopted the Regulation on the Management of Customized Medical Devices in 2019. These regulations cover design, processing, monitoring, and record-keeping, introducing a special regulatory path for 3D printing applications in rare diseases. Additionally, standards focus on risk identification and management over the device lifecycle. Industry associations like the Additive Manufacturing Medical Devices Committee (AMMDC) play a crucial role in developing group standards [31].

South Korea’s MFDS enforces rigorous requirements for 3D-printed medical devices, covering orthopedics, dentistry, and bioprinting. These devices must meet the same safety and performance standards as traditional devices, and manufacturers are required to implement ISO 13485-compliant quality management systems. Recent MFDS guidelines also address good manufacturing practices specific to 3D printing [28].

Regarding device classification, Class I devices—such as anatomical models used for visualization and surgical planning—are considered low risk. Class II devices include digitally enhanced models used for simple surgical planning, often approved via premarket notification. Class III devices, which include complex surgical guides and implants produced via detailed digital planning, are subject to the most stringent controls. The Humanitarian Use Device program offers an alternative approval pathway for devices intended for rare conditions [36].

FDA guidance also includes device surveillance programs and a pre-submission process, although regulatory pathways for 3D printing remain complex and challenging to navigate [32].

Internationally, ISO 13485 establishes quality management system standards essential for manufacturers to ensure safety and regulatory compliance [34]. Complementary standards, such as ISO 10993, assess the biocompatibility of materials used in medical devices and are adopted for Class III device approvals [32].

The ISO/IEC JTC 1 technical committee develops IT standards for 3D printing and scanning, with specific working groups focusing on medical applications. Coordination with ISO technical committees on surgical implants ensures comprehensive coverage of additive manufacturing standards from image processing to implant safety [28].

Prior to formal 2016 guidelines, 3D-printed medical devices in the USA were regulated mainly through premarket notification [510(k)] and new drug application (NDA) processes [31].

Patient data protection is ensured under regulations such as HIPAA, consistent with standards across medical practices [33].

Our findings verify the broad application of 3D printing in orthopedics and related surgical fields, including cardiovascular and plastic surgery. Benefits range from improved anatomical visualization and surgical planning to the development of patient-specific guides and implants [28, 38–44]. These advantages are well-documented and align with emerging trends in personalized medicine [4, 5]. However, their routine clinical adoption remains limited [4, 26].

Despite its promise of enhancing surgical precision and training, 3D printing faces several barriers hindering widespread clinical integration:

1.

High initial and operating costs: The expense of high-quality printers and biocompatible materials remains a significant obstacle [26, 28].

Explicitly outlining these barriers clarifies the previously mentioned “challenges and gaps in practical implementation” and addresses the reviewer’s concerns.

Correlation analyses show a strong link between 3D printing and medical applications, yet reveal a disconnect with quality standard adherence. This highlights the urgent need for research bridging technical innovation and clinical quality assurance [27]. Moreover, the sparse reference to quality indicators in the literature points to a deficiency in process-oriented evaluation metrics [21, 22].

To sustainably implement 3D printing in clinical settings, hospitals should:

Establish clear standard operating procedures (SOPs) for image acquisition, segmentation, printing, and post-processing [32, 33].

To further advance the field, future research should explore the integration of AI into key stages of the 3D printing workflow. AI-based algorithms offer substantial potential in automating segmentation, optimizing print parameters, and predicting material performance, thereby enhancing process efficiency and reproducibility [28, 45]. In addition, the application of biological 3D printing in orthopedics—particularly for cartilage regeneration, bone scaffolding, and meniscal repair—has shown promise in preclinical studies and may play a pivotal role in the development of implantable, patient-specific tissue constructs [12, 13]. Moreover, to address the evolving complexity of clinical and regulatory environments, innovative quality assessment frameworks are needed. We propose the future development of hybrid quality metrics that combine mechanical testing, biocompatibility profiling, and AI-driven dimensional accuracy validation tools. These could support real-time quality control and regulatory alignment, thereby reinforcing safety, consistency, and clinical translation [31, 33, 36].

This review is based on literature analysis without direct input from manufacturers or regulators, which may underrepresent real-world implementation strategies. Furthermore, the focus on English, German, and Spanish publications might exclude relevant findings in other languages.

3D printing demonstrates significant potential to advance orthopedic and traumatological surgery by enabling personalized and precise interventions. However, realizing this potential requires overcoming systemic challenges, including regulatory ambiguity, insufficient interdisciplinary training, and the absence of standardized quality assurance frameworks. Addressing these critical gaps is imperative to fully integrate 3D printing into routine clinical practice and to unlock the benefits of this rapidly evolving technology for improved patient outcomes.