2.1 Study design

This study was designed as a narrative review and conceptual analysis exploring the current and emerging applications of Industry 6.0 technologies in trauma and orthopaedics. The methodology followed the principles of scholarly scoping reviews, focusing on technology convergence, clinical applicability, and translational potential within the orthopaedic domain.

2.2 Search strategy

A comprehensive electronic search was performed in major academic databases, including PubMed, Embase, Scopus, and Web of Science, to identify relevant literature published up to October 2025. The search combined medical and technological keywords related to Industry 4.0, 5.0, 6.0, artificial intelligence, robotics, digital twins, IoT, and orthopaedics. Additional grey literature was retrieved from Google Scholar, WHO Global Digital Health Reports, and manufacturer white papers. References of key publications were manually screened to identify additional sources. Only articles in English focusing on human applications, translational technologies, or orthopaedic relevance were included. Editorials, animal studies, and non-relevant engineering-only papers were excluded.

2.3 Data extraction and synthesis

Identified articles were screened by relevance and categorized based on technological themes; artificial intelligence and machine learning, robotics, digital twins, additive manufacturing, smart implants, and extended reality systems. A narrative synthesis was performed to summarize its evolution, current clinical applications of each technology in trauma and orthopaedics, reported advantages, limitations, and regulatory considerations and future trajectories and integration models under the Industry 6.0 framework (Fig. 1, Table 1).

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Fig. 1 Evolution of orthopaedics across industrial eras from industry 4.0 to 5.0 to 6.0.

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2.4 Research objectives (RO)

The primary and secondary objectives of this review were defined as follows:•RO1: To describe the core technological pillars of Industry 6.0 relevant to healthcare, focusing on AI, robotics, IoT, digital twins, and bioinformatics.•RO2: To evaluate the current status of these technologies and their adoption in trauma and orthopaedic care.•RO3: To identify barriers, ethical considerations, and system-level challenges in implementing Industry 6.0 tools in orthopaedics.•RO4: To explore future directions; including human–AI co-surgery, hyper-personalized implants, and precision orthopaedics, within the Industry 6.0 paradigm.

2.5 Ethical considerations

As this review utilized publicly available data and did not involve human or animal subjects, formal ethical approval was not required.

3.1 Artificial intelligence and machine learning

In the context of Industry 6.0, artificial intelligence (AI) and machine learning (ML) have evolved beyond tools for automation to become systems capable of continuous learning, adaptation, prediction, and personalization of care. In orthopaedics, these capabilities span multiple domains.⁹ AI-driven fracture detection and classification has demonstrated high accuracy on radiographs and CT scans, often matching or exceeding specialist performance. One review indicated that over 24 % of AI applications in orthopaedic trauma focused on fracture detection, with an additional 12 % dedicated to classification.¹⁰

Beyond diagnostics, AI facilitates prognostic modelling, predicting outcomes such as fracture healing, risk of complications like non-union or infection, and even mortality. Predictive models have been shown to outperform traditional scoring systems, with reported improvements in area under the curve (AUC) of approximately 0.10–0.15.¹⁰ Furthermore, AI contributes to treatment planning and optimization by integrating large datasets encompassing imaging, patient demographics, and biomechanical factors, assisting surgeons in selecting implants, alignment strategies, and rehabilitation pathways.¹¹ Finally, AI supports education and workflow enhancement, including research synthesis, operative planning, and resident training.

The relevance of AI in Industry 6.0 lies in the transition from isolated applications to autonomous, integrated systems embedded throughout the entire care pathway; from diagnosis and surgical planning to intraoperative guidance, rehabilitation, and long-term monitoring.⁹ These systems interact synergistically with digital twins, IoT-enabled devices, and robotic platforms, enabling real-time adaptation and hyper-personalization of care.¹² Only approximately 14.5 % of AI studies in orthopaedic trauma have undergone external validation, highlighting concerns regarding generalizability.¹⁰ Moreover, issues of transparency and explainability remain central, as clinicians and regulators require interpretable AI outputs to ensure safe and trustworthy integration into clinical practice.¹³

3.2 Robotics and autonomous/semi-autonomous surgery

Robotics in orthopaedics has progressed from simple assistive devices to fully integrated systems that combine navigation, real-time feedback, haptics, and, in the emerging Industry 6.0 paradigm, autonomous decision support. These advancements are transforming the precision, safety, and personalization of orthopaedic surgery.

One major application is precision implant placement and alignment. Systematic reviews of robotic-assisted orthopaedic surgery have consistently reported improved accuracy of component implantation and surgical planning compared to conventional methods.¹⁴ In trauma and spine surgery, hybrid navigation–robotic “suites” are gaining traction. For instance, in a series of 210 patients treated in a fully equipped “Hybrid-3D Robotic Suite” demonstrated promising operative and postoperative outcomes, highlighting the potential for integrated robotic environments in complex orthopaedic procedures.¹⁵

Robotic systems also contribute to reduced intraoperative trauma. Studies in arthroplasty indicate that robotic-arm assisted total knee arthroplasty (TKA) can lead to lower blood loss and faster functional recovery, likely due to more precise bone cuts and soft tissue preservation.¹⁶ Looking forward, Industry 6.0 envisions autonomous workflows in which robotic platforms dynamically adapt intraoperatively, integrating sensor data, predictive analytics, and potentially adjusting trajectories and strategies in real time under surgeon supervision.

The relevance of robotics in orthopaedics is particularly high given the critical importance of fracture fixation, alignment, load transfer, and joint biomechanics. When combined with AI and digital twin models, robotic systems enable enhanced surgical precision, patient-specific optimization, and predictive outcome planning.

3.3 Digital twins

A digital twin is a virtual replica of a physical entity; such as patient anatomy or implant biomechanics; that can be leveraged for simulation, prediction, and real-time feedback. In orthopaedics, digital twins offer a unique avenue for patient-specific modelling, surgical planning, and postoperative optimization.

Through the integration of imaging, motion capture, musculoskeletal modelling, and finite-element analysis, digital twins enable scenario testing for various clinical decisions. For example, they can simulate fracture fixation strategies or predict implant stress distributions. One study demonstrated the feasibility of applying this workflow in complex trauma revision surgery, highlighting the potential for individualized surgical planning and risk assessment.¹⁷

Digital twins also facilitate surgical planning and rehearsal, allowing surgeons to test “what-if” scenarios, including variations in implant type, osteotomy trajectories, and alignment strategies.¹⁸ Postoperatively, physiologic and biomechanical data; such as load and joint movement; can feed back into the digital twin, enabling dynamic prediction of healing outcomes or risk of implant failure.¹⁸

Furthermore, digital twins provide a powerful tool for training and education, especially when integrated with AR/VR platforms. Surgical trainees can perform immersive simulations on patient-specific models, enhancing skill acquisition without patient risk.

The Industry 6.0 paradigm advances digital twins beyond static simulation. By combining real-time IoT and implant sensor data with AI analytics, digital twins become part of a continuous learning ecosystem, supporting hyper-personalized, adaptive orthopaedic care.

3.4 Advanced 3D printing, biomaterials, and custom implants

In trauma and orthopaedics, the ability to create patient-specific implants and surgical guides is becoming increasingly critical.¹⁹ Industry 6.0 expands this capability by integrating advanced manufacturing, biomaterials, and real-time feedback into a continuous care loop.

Patient-specific surgical guides and implants are now routinely designed using 3D printing technologies.²⁰ Custom cutting guides, fixation plates, and prosthetic components can be tailored precisely to a patient's anatomy and pathology, enhancing surgical accuracy and implant fit.^21,22

Beyond geometry, advanced biomaterials and bioprinting are key components of Industry 6.0. Smart biomaterials; including responsive scaffolds and implants embedded with sensors; enable dynamic monitoring and adaptation. Bioprinting techniques allow the fabrication of cartilage, bone, and composite tissues for regenerative applications, moving beyond simple mechanical reconstruction toward functional restoration.^23,24

The integration of rapid iteration and feedback loops further elevates clinical utility. By linking 3D-printed implants with digital twins and robotic surgical planning, designs can be simulated for biomechanical performance, manufactured, implemented intraoperatively, and subsequently monitored postoperatively. This closes the loop between planning, execution, and outcome assessment, enabling hyper-personalized orthopaedic care.

The implications in clinical practice are significant. For complex fractures, deformities, and revision arthroplasty; particularly in cases of massive bone loss; custom implants can improve anatomical fit, reduce the risk of revision surgery, and accelerate rehabilitation.¹⁹

3.5 IoT and smart devices (wearables and smart implants)

A defining feature of Industry 6.0 is the integration of connected devices that continuously monitor, adapt, and feed data into a dynamic healthcare ecosystem. In orthopaedics, these devices range from wearable sensors to smart implants, enabling real-time, personalized patient care.²⁵

Wearables, including motion sensors, gait trackers, and activity monitors, are increasingly used for remote monitoring of rehabilitation and early detection of complications.²⁶ Subtle changes in gait patterns or activity levels may indicate implant loosening, infection, or delayed recovery. A recent review highlighted that IoT-enabled wearables and smart implants facilitate continuous monitoring and early intervention, enhancing patient safety and outcomes.²⁷

Smart implants embed sensors, data-logging capabilities, and potentially wireless transmission modules within prostheses or fixation devices.²⁸ Systematic reviews have documented applications of smart spinal implants, demonstrating their utility in monitoring load, strain, and physiological parameters, which can inform clinical decisions postoperatively.²⁵

Edge computing and real-time analytics are critical to harness the data generated by these devices. Continuous streams from wearables and smart implants can feed into cloud or edge analytics platforms, which interface with digital twin models, robotic systems, and AI-driven predictive modules to provide actionable insights.²⁹

Beyond monitoring, these systems enhance rehabilitation and patient engagement. Connected devices allow personalized rehabilitation programs, remote physiotherapy supervision, and data-driven assessment of compliance and load management during healing.³⁰ This represents a paradigm shift from traditional “one-time surgery and discharge” toward continuous, adaptive care and iterative optimization of the implant–patient ecosystem.

3.6 Augmented reality (AR), virtual reality (VR), and immersive interfaces

Immersive technologies, including augmented reality (AR) and virtual reality (VR), are increasingly applied across surgical planning, intraoperative navigation, and training in orthopaedics.³¹

AR in surgery allows real-time overlay of patient imaging, implant trajectories, and anatomical structures directly onto the surgical field.³² This capability guides fracture fixation, arthroplasty, and osteotomy procedures, enhancing precision and reducing intraoperative errors. VR for planning and training enables virtual rehearsal of surgeries, simulation of rare or complex trauma cases, and creation of immersive rehabilitation environments for patients. Trainees and clinicians can practice procedures in a risk-free environment, improving skills and confidence before performing live operations.³³

Within the Industry 6.0 paradigm, AR/VR interfaces facilitate human–machine collaboration. Surgeons, robotic platforms, and AI-driven systems can interact with a shared digital twin, allowing synchronized visualization and collaborative decision-making. This integration enhances fracture reduction accuracy, improves implant alignment, and supports education across hierarchical levels; from surgeons and registrars to allied health professionals.

3.7 Blockchain, data security, and interoperability

Although less explored in orthopaedics to date, Industry 6.0 emphasizes secure, interoperable data flows across the entire care continuum. Blockchain and distributed ledger technologies offer mechanisms to ensure data provenance, integrity, and transparency, addressing critical challenges as orthopaedic care becomes increasingly data-driven.³⁴

One application is secure patient data exchange, where imaging, implant information, surgical workflows, and outcome datasets can be recorded on tamper-proof ledgers.³⁵ This ensures the authenticity and traceability of clinical data across institutions and systems. Similarly, implant traceability can be enhanced by embedding a digital identity within each implant; customized or standard; documenting manufacturing details, implantation, and revision history.³⁶

Blockchain also enables audit and tracking of outcomes. Data from smart implants, wearable devices, and clinical assessments can be securely aggregated and analyzed, facilitating research, regulatory compliance, and post-market surveillance.³⁷

The relevance of these systems in orthopaedics is growing as the field becomes increasingly data-rich, with continuous streams from wearables, smart implants, digital twins, and AI decision-making platforms. Ensuring data security, patient privacy, integrity, and adherence to regulatory frameworks is critical for safe and ethical adoption of Industry 6.0 technologies.³⁸

3.8 Sustainability, resource optimization, and human-centric systems

Industry 6.0 emphasizes not only technological advancement but also sustainability, human–machine symbiosis, and hyper-personalization. In orthopaedics, these principles translate into efficient, adaptive, and patient-centered care pathways.³⁹

Resource optimization in operating rooms (ORs) can be achieved through the integration of AI, IoT, and robotics. These technologies enable optimized instrument utilization, energy consumption reduction, minimized surgical waste, and more efficient surgical scheduling. By monitoring and predicting workflow demands, hospitals can enhance throughput while reducing environmental impact.⁴⁰

Human–machine collaboration is central to Industry 6.0. Rather than replacing clinicians, systems are designed to augment surgeon capabilities, integrate surgeon–machine teams, support decision-making, and preserve human judgment. This ensures that expertise and intuition remain integral to patient care while leveraging technological precision.⁴¹ Personalized care trajectories are enabled by data-driven approaches. From implant design and surgical strategy to individualized rehabilitation programs, Industry 6.0 emphasizes the patient as a unique system, moving beyond one-size-fits-all treatment paradigms.

Sustainability extends to implants and biomaterials, with a focus on recyclable materials, lifetime monitoring, and predictive maintenance via IoT sensors. These strategies reduce revision rates, minimize waste, and improve long-term cost-effectiveness. In orthopaedics, particularly given rising volumes of joint replacements, trauma fixations, and an aging patient population, efficient resource use, complication prevention, and personalized rehabilitation are critical to both clinical outcomes and healthcare economics.⁴²

4.1 Artificial intelligence in fracture detection and decision support

AI-powered diagnostic systems are being rapidly adopted for musculoskeletal imaging interpretation. Tools such as BoneView (by Gleamer, France) and Arterys MICA employ deep learning algorithms to detect fractures on X-rays and CT scans with radiologist-level accuracy. Studies have shown significant improvements in diagnostic sensitivity and workflow efficiency, particularly in emergency and trauma settings.⁴³ Beyond detection, machine learning models are now being trained to predict non-union, infection, and implant failure; supporting data-driven treatment decisions.⁴⁴

4.2 Robotic-assisted arthroplasty and trauma surgery

Robotic systems such as MAKO (Stryker), ROSA (Zimmer Biomet), and NAVIO (Smith and Nephew) have become standard tools in knee and hip arthroplasty. These platforms use real-time feedback and haptic control to ensure accurate bone cuts, optimal implant positioning, and reproducible outcomes. Early studies suggest improved alignment accuracy and reduced variability in component placement compared with manual techniques.⁴⁵ In trauma, emerging robotic prototypes are being explored for fracture fixation, especially in pelvic and spine surgery, where precision and navigation are critical.⁴⁶

4.3 Digital twin simulations for preoperative planning

Digital twin technology enables creation of patient-specific anatomical and biomechanical models that can simulate fracture behavior, implant stress, and healing trajectories.⁴⁷ Platforms like Siemens Healthineers' Digital Twin Framework and Dassault Systèmes’ Living Heart and Living Bone projects demonstrate how such simulations can optimize preoperative planning and training. In orthopaedics, digital twins are being evaluated to predict joint kinematics and prosthesis performance under various physiological loads.⁴⁸

4.4 Smart orthopaedic implants and IoT integration

Smart implants with embedded sensors are transforming postoperative monitoring. The Persona IQ smart knee (Zimmer Biomet) incorporates the Canary Health sensor to continuously collect gait, range-of-motion, and activity data.⁴⁹ These data streams are transmitted securely to clinicians for remote follow-up and early detection of complications.⁶

In trauma, ongoing research explores load-sensing intramedullary nails and plates that measure strain across healing fractures, offering real-time insights into bone consolidation and implant stress.

4.5 Immersive and extended reality for training and surgery

AR and VR are being used for preoperative planning, intraoperative navigation, and surgical education. The Microsoft HoloLens integrated with software such as SurgicalAR allows surgeons to overlay 3D reconstructions directly onto the operative field, improving accuracy during complex fracture reconstructions.⁵⁰ Studies have demonstrated significant improvements in spatial awareness and implant placement accuracy.⁵¹ In education, VR simulators like Osso VR and Touch Surgery enable skill development and rehearsal in risk-free environments, aligning with the human–machine collaborative principles of Industry 6.0.^52,53

5.1 High cost and technology adoption issues

The initial capital investment required for robotic platforms, AI infrastructure, and digital twin modeling remains prohibitively high for many institutions, particularly in low- and middle-income countries. Systems such as MAKO or ROSA can cost over USD 1 million, excluding maintenance, licensing, and training expenses.⁵⁴ Additionally, the learning curve associated with robotics, AR/VR systems, and sensor-enabled devices can delay implementation and affect early surgical outcomes.⁵⁵ Resistance among clinicians, stemming from concerns about autonomy, accountability, and workflow disruption, further complicates adoption.

5.2 Data privacy and cybersecurity concerns

The increasing digitization of orthopaedic care through IoT devices, AI systems, and smart implants introduces significant risks to data security. Protected health information (PHI) transmitted between devices and cloud systems is vulnerable to breaches and cyberattacks. In 2023 alone, healthcare accounted for nearly 25 % of all reported data breaches globally.⁵⁶

Ensuring compliance with international standards such as HIPAA, GDPR, and ISO/IEC 27001 is critical but often challenging due to cross-border data sharing, proprietary algorithms, and unclear data ownership in AI-assisted surgical platforms.⁵⁷

5.3 Regulatory hurdles and standardization gaps

The rapid pace of technological innovation outstrips the ability of regulatory frameworks to adapt. Approval pathways for AI-driven diagnostic software, robotic systems, and digital twins remain inconsistent across regions. For instance, the U.S. FDA's Digital Health Center of Excellence has introduced adaptive approval models for machine learning-based devices, yet harmonized global standards are still lacking.⁵⁸

Moreover, there are few standardized metrics for validating AI accuracy, robotic precision, or digital twin fidelity in orthopaedics.¹⁸ This lack of uniform benchmarks hinders clinical validation, multicenter adoption, and long-term safety evaluation.

5.4 Integration into existing healthcare workflows

Integrating advanced technologies into established hospital information systems, electronic health records (EHRs), and surgical workflows poses a significant challenge. Fragmented data architecture, lack of interoperability among platforms, and limited cross-vendor compatibility prevent seamless information exchange.⁵⁹

Furthermore, the introduction of AI and robotic tools often demands restructuring of operative teams, redefined clinical roles, and reallocation of responsibilities; processes that require both cultural and administrative adaptation.⁶⁰ Without coordinated efforts in workflow redesign and training, technological sophistication may paradoxically reduce efficiency or increase cognitive load on surgeons.

6.1 Human–AI Co-surgery and autonomous fracture reduction

The next generation of surgical robotics will go beyond assistance to true human–AI collaboration, wherein robots dynamically adapt their actions in response to intraoperative conditions.⁶¹ AI-driven perception systems using real-time imaging, haptic feedback, and predictive modeling may enable autonomous fracture reduction and fixation with millimeter precision.⁶²

Systems such as the STAR (Smart Tissue Autonomous Robot) have already demonstrated autonomous suturing and soft tissue manipulation with superior consistency to human performance.⁶³ Extending these principles to orthopaedic robotics could yield platforms capable of performing reductions or drilling trajectories based on biomechanical optimization rather than static templates, while still supervised by human surgeons for safety and ethics.

6.2 Hyper-personalized implants and rehabilitation pathways

The convergence of AI, digital twins, and additive manufacturing will enable implants and prostheses designed uniquely for each patient's anatomy, bone density, and activity profile. Using integrated imaging, gait analysis, and mechanical modeling, algorithms can generate hyper-personalized implants that optimize load distribution and osseointegration.⁶⁴

For example, AI-enhanced generative design frameworks developed by companies like Autodesk and Siemens are already being used to produce orthopedic implants with topologically optimized structures mimicking cancellous bone. In parallel, rehabilitation programs may leverage adaptive machine learning models that adjust exercise intensity and frequency based on real-time feedback from wearable sensors, promoting faster and safer recovery.⁶⁵

6.3 Integration of genomics, microbiome, and multi-omics for precision orthopaedics

Industry 6.0's data fusion capabilities will extend beyond biomechanics to encompass molecular-level personalization. Integrating genomics, transcriptomics, proteomics, and microbiome data can revolutionize risk assessment, implant selection, and bone healing prediction. For instance, genomic biomarkers linked to osteoporosis, infection susceptibility, or impaired fracture healing could guide individualized treatment protocols and implant coatings.⁶⁶

Emerging research in bone microbiome and immune–skeletal interactions indicates potential for targeted modulation of healing responses and implant integration.⁶⁷ When combined with AI-driven analytics, these multi-omic datasets could power predictive models for patient-specific outcomes, truly realizing the vision of precision orthopaedics.^68,69

6.4 Sustainable and equitable innovation

A crucial future direction is the emphasis on green orthopaedics; designing sustainable manufacturing and recycling systems for implants, minimizing surgical waste, and reducing the carbon footprint of digital infrastructure.^70,71 Equally important is ensuring global equity in access to Industry 6.0 technologies. Collaborative frameworks between governments, academia, and industry will be necessary to prevent a “digital divide” in orthopaedic care between resource-rich and resource-limited regions.