The appeal of 3D printed splints is easy to understand. Because each device is designed from a digital scan of the individual patient’s anatomy, it can be shaped to fit precisely while remaining far lighter than a conventional cast. The printed structure can be open and lattice-like, allowing air to circulate against the skin. That improved aeration reduces the moisture buildup and skin irritation that plague cast wearers, and it also improves hygiene around the injury. Perhaps most importantly for trauma care, an open splint leaves room for wound treatment: dressings can be changed, sutures can be inspected, and infections can be managed without cutting the device off and starting over. For patients, that can mean a dramatically more comfortable recovery and fewer return visits to the emergency department.

Yet despite these advantages, patient-specific printed splints remain rare in daily clinical practice. The reason is not that the technology does not work — small studies and prototype programs have demonstrated it repeatedly — but that getting from a working prototype to a routine hospital service is genuinely hard. The Nijmegen team, which included trauma surgeons, 3D printing specialists, and a biomechanical engineer from the University of Twente, identified three intertwined categories of difficulty. There are technical and logistical challenges, such as choosing scanners, design software, and printers that can produce medically acceptable devices reliably. There are workflow adjustments, because the path from patient to finished splint must fit into the fast-paced routines of an acute care setting. And there are regulatory constraints, because a hospital that manufactures its own medical devices steps into the role of a device producer under European medical device regulation, with all the documentation and quality obligations that entails.

To confront these barriers, the researchers did something unusually practical for an academic paper: they ran the implementation on-site, at a level-one trauma center with military affiliation in the Netherlands. Rather than designing a theoretical framework in an office, they organized multidisciplinary workshops in which the people who would actually operate the service — trauma surgeons, 3D lab staff, and other stakeholders — sat down together to identify, document, and discuss every essential obstacle they encountered. The workshops served as a structured forum for surfacing problems that might otherwise remain invisible until they caused a failure: who is responsible when a printer breaks mid-production, how a scan moves from the emergency room to the design workstation, which software licenses the hospital must hold, and how the finished device is documented for regulatory purposes.

The output of this process was a taxonomy of challenges. The team categorized the difficulties into essential barriers, each with multiple subitems, and then translated every barrier into a concrete step in a roadmap. Each step specifies the stakeholders involved and the actions required, so that a hospital following the guide knows not just what must be done but who must do it. Examples of the barriers include software and hardware selection for in-house splint production — decisions that ripple outward into training needs, maintenance contracts, and quality assurance. Other barriers concern the clinical workflow, the regulatory pathway, and the organizational changes needed to make in-house manufacturing sustainable rather than a one-off demonstration project.

The regulatory dimension deserves particular attention, because it is where many hospital innovation projects stall. Under the European Medical Device Regulation, a hospital producing custom-made devices in house is not merely using a product; it is manufacturing one. That role carries requirements for documentation, production standards, and traceability that most clinical departments have never had to meet. The roadmap addresses this by embedding the regulatory steps directly into the implementation sequence, so that quality obligations are built in from the start rather than retrofitted after problems arise. This is a significant shift from the way many hospitals have approached 3D printing, treating it as a research activity with looser obligations rather than a clinical service with strict ones.

The military affiliation of the participating hospital adds an interesting layer to the work. Military medicine has long been an early adopter of portable and on-demand manufacturing, since deployed units cannot rely on supply chains for specialized equipment. The involvement of the Military Health Organisation of the Dutch Ministry of Defence, reflected in the author affiliations, suggests that the roadmap was shaped by perspectives from both civilian trauma care and military health logistics. A hospital that can print its own splints from digital scans is also a hospital that can maintain essential capability when external suppliers are unavailable — a resilience argument that resonates well beyond the trauma ward.

To make sure the roadmap was actually usable by the people it is meant for, the researchers put the finished implementation guide in front of end-users and collected their feedback through a questionnaire. This review step is more than a courtesy; it is a check on whether the document communicates clearly to clinicians and technical staff who were not part of the workshops. The team reports that the roadmap provides a structured approach to meeting the requirements for implementation into both research and daily clinical practice — a dual purpose that matters, because many hospitals will want to run a research program first, gather evidence on safety and effectiveness, and only then transition to routine service.

For hospital administrators reading the study, the practical message is that in-house 3D printing of patient-specific splints is achievable, but only with deliberate planning across multiple domains simultaneously. The roadmap’s structure — essential barriers broken into subitems, each mapped to stakeholders and actions — gives institutions a checklist for the conversations they must have before buying a single printer. Which department owns the service? Who designs the devices, and with what software? How are scans acquired, stored, and protected? What documentation satisfies the regulator? How are staff trained, and how is the service maintained when key personnel leave? None of these questions is individually difficult, but the study’s central insight is that they must all be answered together, in a coordinated sequence, for the implementation to succeed.

The broader significance of the work lies in what it represents for hospital-based manufacturing as a whole. Splints are a relatively simple class of medical device, which makes them an ideal proving ground for the organizational machinery of in-house production. A hospital that masters the workflow, regulatory, and quality challenges of printed splints has built the institutional capability to produce other custom devices — surgical guides, prosthetic components, and beyond. The Nijmegen team’s roadmap, born from the friction of real implementation rather than from theory, offers other institutions a way to skip some of that friction. As 3D printing hardware becomes cheaper and design software more accessible, the limiting factor in bringing personalized devices to patients is increasingly not the technology but the organization around it — and that is precisely the problem this study set out to solve.

Subject of Research: In-house production of patient-specific 3D printed splints in acute care

Article Title: An implementation roadmap for in-house production of patient-specific 3D printed splints in an acute care setting

Article References: van Ginkel, L. A., de Bruijn, M. A. N., Hermans, E., Maal, T. J. J., Stirler, V. M. A., & Tuijthof, G. J. M. (2026). An implementation roadmap for in-house production of patient-specific 3D printed splints in an acute care setting. 3D Printing in Medicine. https://doi.org/10.1186/s41205-026-00343-9

Image Credits: AI Generated

DOI: 10.1186/s41205-026-00343-9

Keywords: 3D printing, patient-specific splints, trauma surgery, medical device regulation, implementation roadmap, acute care, hospital manufacturing, 3D Printing in Medicine, Radboudumc, custom medical devices, clinical workflow, immobilization

The study, led by Joseph Conteh of the University of New Mexico together with Naim Kapucu, Eunice F. Ebevuhe, and Oluwapelumi Wumi Adeniji, was conducted and reported in accordance with the PRISMA 2020 reporting guideline, with study selection governed by predefined PICOS eligibility criteria. The team searched four databases—PubMed, Web of Science, Scopus, and Google Scholar—for peer-reviewed articles published from January 2017 through January 2026, with final searches completed between January 15 and 18, 2026. Of 1,065 records identified, just 22 studies met the eligibility criteria. Methodological quality was appraised with design-appropriate tools: the Critical Appraisal Skills Programme checklist for qualitative and certain observational designs, the Newcastle-Ottawa Scale for cohort and case-control studies, and QUADAS-2 for diagnostic accuracy studies. A narrative synthesis was followed by a structured regulatory gap analysis of federal statutes, regulations, and agency guidance, in which a gap was recorded only when at least two independent sources converged. Inter-rater agreement at the title and abstract screening stage was substantial, with Cohen’s kappa of 0.84.

The review’s central diagnosis is that the federal regulatory architecture was built for a fundamentally different technological paradigm. Authority over hospital AI is dispersed across agencies that each hold a partial slice of oversight: the FDA evaluates safety and effectiveness of Software as a Medical Device through pre-market review; the Office of the National Coordinator for Health Information Technology certifies health IT and mandates transparency for predictive decision-support interventions through the HTI-1 rule; the Centers for Medicare and Medicaid Services shapes adoption through conditions of participation, quality reporting, and payment policy; HIPAA governs the privacy and security of health data; and the Federal Trade Commission polices deceptive or unfair AI claims. In a decentralized federal environment, state legislation has emerged as a supplementary force, producing a patchwork landscape—California, for example, has established some of the nation’s strictest privacy and transparency standards, requiring healthcare institutions using AI-assisted diagnostic tools to inform patients about the system’s functionality, diagnostic basis, and potential risks.

The most technically striking finding concerns the chasm between pre-market validation and real-world performance. A COVID-19 chest radiograph AI diagnostic tool that achieved an area under the receiver operating characteristic curve greater than 0.95 on open datasets dropped to approximately 0.70 after prospective deployment across multiple hospital settings, varying from 0.66 to 0.74 across time quartiles over 19 post-deployment weeks. Predictive models delivering AUC values of 0.96 for readmission prediction and 0.83 from roughly 280,000 cases were tested only on retrospective data without external validation. The review is careful to distinguish deficiencies in the evidence submitted to the FDA from deficiencies in FDA requirements themselves—many validation gaps originate in the pre-submission research pipeline rather than in statutory standards, a distinction that matters because the remedies differ. Even stringent validation systems such as APPRAISE-AI, which rates methodology, results, and reproducibility, do not fully account for temporal and situational variation in clinical settings.

When regulatory-grade evidence does exist, the results can be dramatic. The KATE triage system outperformed nurses by 26.9% in general acuity assignment across 729 reviewed patient records, decreased undertriage from 19.8% to 9.7% and overtriage from 20.4% to 14.4%, and captured 1,880,841 features at 12.79 per encounter with 98.47% accuracy. An FDA-authorized AI tool for pulmonary embolism detection achieved 99.9% negative predictive value across 11,736 real-world imaging tests and slashed worklist detection times from 7,714 minutes to 87 minutes while reducing missed cases from 44.8% to 2.6%. A machine learning sepsis prediction algorithm enabled antibiotic delivery within 2.76 hours, cutting in-hospital mortality from 21.3% to 9.0%—a 58% relative reduction. But these successes remain exceptions rather than the norm, and the review found that AI’s relationship with CMS quality metrics is mixed: in a longitudinal observational study spanning 70 CMS quality indicators from 2022 to 2025, hospitals using AI showed positive trends for pneumonia mortality and hospital-acquired conditions but negative trends for readmission rates and sepsis treatment compliance. Because these are observational associations, they do not establish causal effects.

The structured gap analysis identified four critical, structurally interconnected failures. The first is the post-market monitoring gap: only 58.1% of hospitals in a national sample performed post-implementation monitoring in 2024, and the majority of those lacked continuous drift detection. In related survey data, 47.4% of hospitals did not evaluate post-deployment accuracy, 51.4% had no bias assessment protocols, and in 61% of hospitals post-deployment accuracy was assessed but bias was not, with all monitoring remaining sporadic. The second is the equity assurance gap. The training dataset of a readmission prediction model included less than 2% Hispanic and Asian participants while Caucasian patients made up more than 75%, with no subgroups evaluated for differential performance. The KATE system showed an 11.4 percentage point accuracy disparity between adults (78.5%) and children (67.1%) without subgroup analyses for race, ethnicity, or socioeconomic status. The review also distinguishes algorithmic bias from structural adoption disparities: larger hospitals had a 15.2% higher probability of adopting machine learning than smaller ones, health system-owned hospitals had a 26.8% higher probability than independently owned ones, and regions of high healthcare need in low-adoption areas had persistently lower adoption probabilities—including ratios of 0.75 for primary care Health Professional Shortage Areas and 0.74 for high Area Deprivation Index areas.

The third failure is the accountability clarification gap, which the review dissects into four frequently conflated dimensions: legal liability, clinical responsibility, institutional governance, and vendor obligations. Radiologists bore responsibility for diagnosing pulmonary embolism, yet responsibility allocation for 12 cases of missed diagnosis remained unclear. An AI-clinician hybrid framework for COVID-19 diagnosis did not determine responsibility distribution among developers, informaticians, and treating physicians, and a randomized clinical trial of sepsis prediction specified no clear distribution of responsibility in case of patient harm. The problem is compounded by market concentration: 79% of hospitals use predictive models created by electronic health record vendors, yet no frameworks exist for determining accountability between hospitals, developers, vendors, and clinicians. The fourth is the governance capacity gap, which operates as a cross-cutting constraint across the others. Only 65% of hospitals used predictive models and only 44% assessed biases, with higher implementation rates concentrated among larger and more profitable institutions. Even best-practice tools illustrate the barrier: DEPLOYR required Azure Functions, specialized Python libraries, data warehouse access with institutional review board approval, and registration at the Epic App Orchard, while the MedPerf federated benchmarking initiative demanded collaboration between more than 20 firms, 20 universities, and 9 hospitals across 13 countries, encountering regulatory, legal, and reputational barriers throughout.

The documented harms lend urgency to these technical findings. The widely cited study by Obermeyer and colleagues demonstrated that a commonly used healthcare algorithm systematically underestimated illness severity among Black patients, resulting in only 17.7% being identified as eligible for advanced care programs when a corrected, unbiased algorithm would have identified 46.5%. Insufficiently externally validated sepsis prediction technologies have generated alert fatigue that paradoxically diminishes clinician responsiveness to genuine patient deterioration signals. Survey data show that 73.1% of clinicians regarded lack of trust, transparency, or bias as critical barriers to implementation, and evaluations of large language models found that GPT-4 provided consistent responses in only 67 of 80 clinical scenarios (83.8%), with both GPT-3.5 and GPT-4 demonstrating weakness in recognizing diversity, equity, and inclusion principles. The review notes that the HTI-1 rule’s transparency mandates, while a positive step, suffer from underdeveloped enforcement mechanisms and unstandardized disclosure content.

International comparisons sharpen the picture. The European Union Artificial Intelligence Act imposes binding pre-market conformity assessment for high-risk AI systems, including most medical AI, and centralizes enforcement in designated market-surveillance authorities while assigning explicit obligations to both providers and deployers. U.S. requirements, by contrast, remain dispersed across FDA, ONC, and CMS authorities, with enforcement divided among federal and state actors holding partial jurisdiction, and post-market mechanisms such as Predetermined Change Control Plans still in early implementation. Drawing on these models, the authors propose a lifecycle governance framework with six evidence-linked components: rigorous pre-market clinical validation with standardized benchmarking, mandatory post-deployment monitoring with automated drift detection and performance alerts, standardized transparency and explainability aligned with the HTI-1 rule, equity-centered assessment protocols requiring subgroup analysis and bias reporting, clear accountability frameworks delineating responsibility among developers, vendors, institutions, and clinicians, and scalable governance infrastructure with targeted support for under-resourced settings.

The authors acknowledge key limitations: the retrospective registration of the review protocol on the Open Science Framework, the restriction to English-language publications, database coverage limited to four sources while Embase, CINAHL, and ClinicalTrials.gov were excluded, and the heterogeneity of the included evidence, much of it retrospective and single-center. International studies from Canada, Saudi Arabia, and the Netherlands were used to contextualize rather than as direct evidence of U.S. regulatory performance. Still, the core conclusion stands on firm ground: industry projections indicate AI-enabled technologies will influence decision-making in most major U.S. health systems within the coming decade, and if the identified gaps persist as deployment scales, the risks to patient safety, equity, and clinical trust will grow commensurately. The review closes by grounding its call in medicine’s oldest ethical principle—primum non nocere, first, do no harm—arguing that only a national, lifecycle-focused, systems-based governance approach can ensure that hospital AI remains safe, ethical, and trustworthy as it reshapes clinical care.

Subject of Research: A systematic review and regulatory gap analysis of AI governance in U.S. hospitals, identifying four critical regulatory gaps and proposing a lifecycle governance framework.

Article Title: A framework for safe, ethical, and trustworthy AI deployment and integration in hospitals: a systematic review and regulatory gap analysis with emphasis on the U.S. regulatory context

Article References: Conteh, J., Kapucu, N., Ebevuhe, E. F., & Adeniji, O. W. (2026). A framework for safe, ethical, and trustworthy AI deployment and integration in hospitals: a systematic review and regulatory gap analysis with emphasis on the U.S. regulatory context. Frontiers in Digital Health, 8, Article 1955958. https://doi.org/10.3389/fdgth.2026.1955958

Image Credits: AI Generated

DOI: 10.3389/fdgth.2026.1955958

Keywords: AI governance, hospital AI, FDA regulation, health equity, patient safety, systematic review, regulatory gaps, digital health, framework, safe, ethical, trustworthy

Hutt joined C-Path’s Board in 2015, bringing with him decades of unparalleled leadership and expertise in food and drug law. At that point, he had already spent the better part of a lifetime shaping the very field in which C-Path operates. The institute, founded in 2005 as a public-private partnership in response to the FDA’s Critical Path Initiative, exists to lead collaborations that advance better treatments for people worldwide. Its consortia and programs currently include more than 1,600 scientists and representatives drawn from government and regulatory agencies, academia, patient organizations, disease foundations, and pharmaceutical and biotech companies. For a body like that, having a board member who had personally helped construct the statutory foundations of drug regulation was an extraordinary asset, and colleagues say Hutt used that position to push the organization toward ever more rigorous thinking about how science and policy meet.

The milestones of Hutt’s career read like a history of modern American food and drug law. From 1971 to 1975, he served as Chief Counsel for the U.S. Food and Drug Administration, a tenure during which he helped transform the agency’s approach to regulation and left a lasting imprint on the modern legal architecture of the field. That period was pivotal for the FDA, and Hutt’s innovative approach to regulation during those years cemented his reputation as a foundational figure in regulatory science. After leaving government, he did not retreat from public service; instead, he became a bridge between the agency, Congress, industry, and the academy, a role he would occupy for the rest of his professional life.

Perhaps no statistic captures his influence better than his legislative record. Over a career spanning more than six decades, Hutt served as a principal architect for most major legislation amending the Federal Food, Drug, and Cosmetic Act, the foundational statute under which the FDA regulates drugs, devices, and food. Every time Congress has modernized that law, in most cases Hutt’s hand was somewhere in the drafting, negotiation, or intellectual groundwork. He also testified before the U.S. House and Senate more than 100 times, a level of engagement with the legislative process that is almost unheard of in any field of law. Few attorneys in any discipline can claim to have shaped their area of practice so directly, across so many decades, and at so many points of leverage.

Hutt was equally committed to education and scholarship. He taught food and drug law at Harvard Law School for more than three decades, training generations of lawyers who would go on to lead regulatory agencies, pharmaceutical companies, public interest organizations, and law firms. He co-authored the leading casebook in the field, Food and Drug Law: Cases and Materials, which became the standard text through which students encountered the subject. Through that combination of classroom teaching and written scholarship, he effectively defined how the discipline is understood and practiced. He was also a longtime member of the Institute of Medicine, now known as the National Academy of Medicine, and maintained an active legal practice and teaching schedule throughout his final years, a testament to the energy he brought to his work well into the later stages of his life.

At C-Path, colleagues emphasize that Hutt’s contribution went far beyond his legal credentials. Klaus Romero, M.D., M.S., FCP, the institute’s CEO, said he had the privilege of knowing Hutt for many years and could say without exaggeration that few people have shaped the practice of food and drug law, and by extension the course of regulatory science, the way Hutt did. Romero described a board member who brought a rare combination of historical perspective and forward-looking judgment, someone who had seen regulatory science evolve across different sectors for over five decades and who used that experience to help guide C-Path through some of the most important years of its growth. According to Romero, Hutt was generous with his time, sharp in his counsel, and genuinely invested in the organization’s mission, always pushing it to think rigorously about how science and policy intersect.

Board Chairwoman Karen Bernstein, Ph.D., offered an equally striking assessment of Hutt’s legacy. She described him as a towering figure in food and drug law whose influence will be felt for generations, noting that he did more than interpret the laws governing medicines, medical devices, and food. In her view, he helped shape the modern regulatory framework itself while educating and mentoring generations of leaders who would carry that work forward. Bernstein credited Hutt with bringing extraordinary intellectual rigor, historical perspective, and a deep commitment to public health to everything he did, and said C-Path was fortunate to benefit from his wisdom and service on its board. She expressed profound gratitude for his enduring contributions to the organization, to the FDA, and to the field of regulatory science.

The significance of Hutt’s work becomes clearer when considered against the backdrop of the institution he served. The Federal Food, Drug, and Cosmetic Act is the legal backbone of American consumer and medical protection, and the FDA’s authority to require evidence of safety and efficacy before a drug reaches patients rests on amendments to that statute. Regulatory science, the discipline of developing the tools, standards, and methods that make such oversight scientifically meaningful, depends on a legal framework that is both robust and adaptable. Hutt’s career embodied that dual demand: as a government lawyer he strengthened the agency’s regulatory approach, as a legislative architect he modernized the statute itself, and as an educator he ensured that the next generation could carry the work forward. His presence on C-Path’s board connected that long legal tradition directly to the institute’s scientific collaborations, which aim to speed the development of better treatments through data sharing, biomarker qualification, and other science-driven approaches.

C-Path itself occupies a distinctive position in the drug development ecosystem. Established in 2005 as a public-private partnership in response to the FDA’s Critical Path Initiative, the institute has grown into a globally recognized pioneer in accelerating drug development, with international consortia and programs spanning regulators, academia, patient advocates, and industry. Its headquarters in Tucson, Arizona, and its European subsidiary in Amsterdam, Netherlands, anchor a network of dedicated team members located throughout the world. The organization is supported by the Food and Drug Administration of the Department of Health and Human Services, which funds 43 percent of its work, totaling $20,724,703, with the remaining 57 percent, totaling $27,346,613, coming from non-government sources. The institute notes that its contents do not necessarily represent the official views of, nor an endorsement by, FDA/HHS or the U.S. Government. Within that structure, Hutt’s decade on the board provided legal and historical ballast for an organization whose work depends on navigating exactly the regulatory terrain he helped build.

Tributes from across C-Path reflect a consistent theme: gratitude. The institute has extended its deepest condolences to Hutt’s family, friends, and colleagues during this time of loss. For the broader community of regulatory scientists, drug developers, and public health advocates, his passing represents the loss of a singular figure, someone who stood at the junction of law, science, and medicine for more than sixty years and who shaped each of them in turn. From his years as FDA Chief Counsel in the 1970s, through his decades at Harvard Law School and his role in drafting the nation’s most important health legislation, to his final years of active practice and service on the C-Path board, Peter Barton Hutt leaves behind a regulatory landscape that bears his imprint at nearly every turn. The organization he served for a decade says it is grateful for everything he gave, and the field he helped create will continue to feel his influence for generations to come.

Subject of Research: The passing of food and drug law pioneer and Critical Path Institute board member Peter Barton Hutt

Article Title: C-Path mourns the passing of Board Member Peter Barton Hutt

Article References: C-Path mourns the passing of Board Member Peter Barton Hutt. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Peter Barton Hutt, Critical Path Institute, FDA, food and drug law, regulatory science, Federal Food Drug and Cosmetic Act, Harvard Law School, drug development, public health, C-Path Board of Directors, obituary, pharmaceutical regulation

The research, led by Mauranne Lievens of the Department of Oral & Maxillofacial Surgery at Ghent University Hospital, together with Wim Van Paepegem, Tom Goffin, Geert Villeirs and Renaat Coopman, addresses a long-standing gap in the validation of hospital 3D printing programs. Earlier validation studies typically relied on geometric calibration artifacts, standardized test shapes, or simplified phantom objects—objects that behave well on a build plate but share little with the irregular, thin-walled, undercut-rich anatomy of a human skull. The Ghent team instead designed the study around highly accurate anatomical models, deliberately mimicking the actual workflow a hospital follows when a surgeon requests a patient-specific replica for preoperative planning, implant shaping, or patient counseling.

Accuracy in this context is not a single number but a composite of two distinct properties. The first is trueness, which measures how closely a printed model matches the digital reference design—the deviation between the physical object and the source file that clinicians use. The second is precision, which captures reproducibility: how consistent a printer is when producing the same model repeatedly within a single build (intra-build variability), and how consistent it is across separate print jobs run at different times (inter-build variability). Both dimensions matter clinically. A printer that faithfully reproduces anatomy once but drifts between builds is as problematic as one that is consistently wrong. The team quantified both using root mean square (RMS) error, a standard metric that aggregates surface deviation across the entire model rather than relying on a handful of caliper measurements at convenient points.

The statistical design of the study is one of its most notable contributions. Because the dataset comprised repeated prints of multiple anatomical models at multiple timepoints, the observations were inherently clustered—prints from the same build and models from the same printer are not independent of one another. Conventional statistical tests that ignore this structure can overstate significance. To handle the complexity, the researchers employed a Linear Mixed Model (LMM), a framework widely used in agriculture, ecology and clinical trials but, according to the authors, never before applied in 3D printing accuracy research. The mixed model allowed the team to evaluate fixed effects—printer type, anatomical model, and comparison type—while correctly accounting for the internal clustering of repeated measurements, yielding a statistically robust picture of where true differences lie.

The headline result is that every technology tested achieved accuracy within clinically acceptable limits, an encouraging finding for hospitals that have invested in lower-cost equipment. But the ranking was clear and statistically significant. Material Jetting, which builds objects by jetting tiny droplets of photopolymer in extremely thin layers, achieved a mean RMS error of 67 micrometers—the finest accuracy in the study. Stereolithography, which cures liquid resin layer by layer with a light source, followed at 109 micrometers. Fused Filament Fabrication, the thermoplastic extrusion process behind most affordable desktop printers, came in at 130 micrometers. In practical terms, these differences span roughly the width of one to two human hairs, yet in surgery that margin can matter: when a model is used to pre-bend titanium plates or rehearse the osteotomy of a complex orbital or mandibular reconstruction, sub-millimeter deviations propagate directly to the operating table.

Just as important as the ranking was what the Linear Mixed Model revealed about the drivers of error. Printer type was a significant fixed effect, confirming that the technology choice itself—not random variation—explains much of the accuracy difference. But anatomical complexity also exerted a significant influence, meaning that the shape being printed matters nearly as much as the machine printing it. Models with delicate facial structures, thin bony walls and deep undercuts stressed each technology differently: resin- and jetting-based photopolymer processes handled fine detail more gracefully, while extrusion-based FFF showed characteristic stair-stepping and filament-path limitations on curved, intricate surfaces. This interaction underscores why calibration artifacts alone cannot predict clinical performance; a flat test coupon tells a hospital little about how a printer will cope with a zygomatic arch or a thin orbital floor.

The study’s framing around the Medical Device Regulation gives it particular weight for European hospitals. MDR 2017/745 classifies many in-house manufactured products as devices and requires health institutions to demonstrate that their production, under the responsibility of a single legal manufacturer, meets safety and performance expectations—including verification of accuracy appropriate to the intended clinical use. Point-of-care 3D printing has often grown organically inside surgical departments, with validation practices varying wildly from one hospital to the next. By defining accuracy through trueness and precision, quantifying it with RMS error against digital references, and analyzing it with a defensible statistical model, the Ghent team has effectively published a template for the kind of documented, reproducible validation that regulators and hospital quality officers can build upon.

For hospital administrators weighing procurement decisions, the results offer a nuanced cost-benefit picture. Material Jetting’s superior fidelity comes with higher machine and material costs, and photopolymer processes require resin handling and post-processing protocols. FFF remains the most affordable and accessible option, and the study confirms its output is still clinically acceptable—suggesting it can retain a role for models where extreme fidelity is not essential, such as patient education or approximate planning. SLA sits between the two, offering strong performance at moderate cost. The finding that all three cleared the clinical acceptability bar means the choice can legitimately be driven by the intended application, budget and workflow, rather than by fear that cheaper technology is automatically unsafe. For applications demanding the highest anatomical fidelity within the MDR framework, however, the evidence now points decisively toward Material Jetting.

The research also carries implications well beyond craniomaxillofacial surgery. In-house and point-of-care 3D printing is expanding rapidly into orthopedics, cardiothoracic surgery, dental applications and surgical training, and virtually every one of those programs faces the same questions: how accurate is our printer, how repeatable is it, and can we prove it? The study’s methodological chain—realistic anatomical models, digitized reference comparisons, RMS-based quantification of trueness and precision, and mixed-model statistics that respect the structure of repeated manufacturing data—offers a portable blueprint for any institution seeking to validate its own fleet. The authors note it is the first application of a Linear Mixed Model in this field, and if the approach is adopted broadly, it could standardize how hospitals across jurisdictions demonstrate regulatory compliance. Published open access in the journal 3D Printing in Medicine, with no specific external funding and no declared competing interests, the study was reviewed with the involvement of the Ghent University Hospital Medical Ethics Committee, receiving official approval on July 2, 2024, and received on 16 September 2025, accepted on 16 August 2026, and published on 11 September 2026.

Subject of Research: Comparative accuracy of FFF, SLA and Material Jetting 3D printing for in-house anatomical models in craniomaxillofacial surgery

Article Title: Clinical relevance of accuracy in in-house 3D printing in craniomaxillofacial surgery: a comparative study of FFF, SLA, and MJ technologies

Article References: Lievens, M., Van Paepegem, W., Goffin, T., Villeirs, G., & Coopman, R. (2026). Clinical relevance of accuracy in in-house 3D printing in craniomaxillofacial surgery: a comparative study of FFF, SLA, and MJ technologies. 3D Printing in Medicine. https://doi.org/10.1186/s41205-026-00344-8

Image Credits: AI Generated

DOI: 10.1186/s41205-026-00344-8

Keywords: 3D printing, craniomaxillofacial surgery, anatomical models, FFF, SLA, Material Jetting, Medical Device Regulation, accuracy validation, Linear Mixed Model, point-of-care manufacturing, Clinical, relevance