Enhancing Biomedical Metal 3D Printing with AI and Nanomaterials Integration

Abstract

The integration of artificial intelligence (AI) with nanomaterials is rapidly transforming metal three-dimensional (3D) printing for biomedical applications due to their unprecedented precision, customization, and functionality. This article discusses the role of AI in optimizing design parameters, predicting material behaviors, and controlling additive manufacturing processes for metal-based implants and prosthetics. Nanomaterials, particularly metallic nanoparticles, enhance the mechanical strength, biocompatibility, and functional properties of 3D-printed structures. AI-driven models, including machine learning (ML) and deep learning algorithms, are increasingly used to forecast print quality, detect defects in real-time, and reduce material waste. Moreover, data-driven design approaches enable patient-specific implant development and predictive modeling of biological responses. We highlight recent advancements in AI-guided material discovery through microstructure–property correlations and multi-scale simulation. Challenges such as data scarcity, standardization, and integration across interdisciplinary domains are also discussed, along with emerging solutions based on federated learning and the digital twinning approach. Further, the article emphasizes the importance of AI and nanomaterials to revolutionize metal 3D printing to fabricate smarter, safer, and effective biomedical devices. Future perspectives covering the need for robust datasets, explainable AI frameworks, and regulatory frameworks to ensure the clinical translation of AI-enhanced additive manufacturing technologies are discussed.

1. Introduction

In recent years, the convergence of artificial intelligence (AI) and nanomaterials has redefined the landscape of metal-based three-dimensional (3D) printing processes in biomedical engineering applications [1]. This synergistic integration has been identified to possess immense promise for the fabrication of next-generation implants and prosthetic devices that are mechanically robust, biocompatible, and customizable based on individual patient anatomies with unmatched precision [2]. Metal 3D printing techniques based on Powder Bed Fusion (PBF), such as laser beam PBF (commonly referred to as selective laser melting (SLM^®)) and electron beam PBF (commonly referred to as EBM^TM), have already enabled the fabrication of intricate structures that are previously unattainable with traditional manufacturing methods [3]. It has been estimated by Global Market Insights in 2022 that the market size of 3D printed implants was around USD 2.5 billion, which was anticipated to reach 19% of compound annual growth rate (CAGR) between the years 2023 to 2032 [4]. However, the addition of AI-driven methodologies and functional nanomaterials is accelerating innovation in the latest 3D printing processes, offering enhanced structural replication [5]. This integration not only enhances implant performance but also creates opportunities for patient-specific customization, combining the strength of nanomaterials with AI-guided process optimization.

Nanoparticle-integrated additives play a key role in advancing metal 3D printing for biomedical implants. Metallic nanoparticles (e.g., silver, titanium, copper), ceramic nanoparticles, and nanostructured alloys can be embedded into printable matrices to improve mechanical strength, wear resistance, corrosion behavior, and biological integration of 3D printed implants [2,6]. Recent studies show that nanoparticle additives enhance antibacterial activity, support osteogenic responses, enable controlled degradation in bioresorbable implants, and allow fine-tuning of surface topography to promote cellular adhesion and proliferation [7]. Despite these advantages, challenges remain, including the optimization of printing parameters for different nanoparticle types and ensuring the reproducibility of mechanical and biological functions. These challenges highlight the need for AI-driven predictive modeling, real-time monitoring, and adaptive control to ensure consistent, high-quality implant fabrication. These material-level enhancements create opportunities for AI-driven optimization, ensuring reproducible, patient-specific implants.

Building on nanoparticle-enhanced implants, AI, especially in the form of machine learning (ML) and deep learning (DL), is revolutionizing additive manufacturing (AM) processes via tools for automated design optimization, defect prediction, and real-time process control [8]. AI is considered a broad concept to create intelligent systems, where ML utilizes algorithms to learn from data without explicit programming approaches. Meanwhile, DL is a subset of ML that uses multilayered, deep neural networks to learn from complex data [9]. These data-driven models can analyze vast, complex datasets to recognize patterns that can avoid conventional simulations for improving the efficiency, repeatability, and safety of implant fabrication via AM or 3D printing processes [7]. Simultaneously, nanomaterials, especially metallic nanoparticles and nanostructured alloys, are embedded into printable matrices to enhance biological integration, antibacterial performance, and mechanical durability of 3D-printed implants [6]. The integration of AI with nanoparticle-enhanced matrices offers opportunities to tailor implants for specific mechanical, biological, and patient-specific requirements, while simultaneously addressing the challenges posed by nanoparticle incorporation. Hence, this review critically explores the impact of integrating AI and nanomaterials in shaping the future of metal 3D printing processes in biomedical applications. Further, the mechanisms through which AI is used to optimize printing parameters, the role of nanomaterials in tailoring implant performance, and the emergence of patient-specific, intelligent implants were also discussed. Furthermore, the review also addresses current challenges such as data scarcity, model generalizability, interdisciplinary fragmentation, and regulatory gaps, and challenges related to nanoparticle dispersion, which limit the clinical translation of these advanced technologies. Emerging solutions, such as federated learning, digital twins, and explainable AI frameworks, are also being introduced as pathways to overcome the limitations of 3D-printed implants. In addition, this review aims to provide researchers, clinicians, and biomedical engineers with a comprehensive understanding of the transformative potential and limitations of AI and nanomaterials in the 3D printing of smart, functional implants. By highlighting the combined impact of AI and nanomaterials, this review provides a comprehensive understanding of how these technologies advance patient-specific biomedical implants and address current challenges.

2. Role of Artificial Intelligence in Metal 3D Printing Application

AI, encompassing ML and DL, is revolutionizing metal 3D manufacturing (also referred to as metal additive manufacturing) processes with significant advancements across the manufacturing approaches [10]. In biomedical contexts, AI has been particularly transformative, enabling the production of implants, prosthetics, and scaffolds tailored to patient anatomy and mechanical requirements (see Section 5 for more details). AI-driven approaches optimize design parameters, monitor and control complex printing processes in real-time, and predict material behavior and outcomes with high accuracy, directly improving clinical outcomes [11]. Patient-specific requirements, complex geometries, and biocompatibility considerations present unique challenges that AI can help address, such as customized implants with optimized mechanical performance, enhancing osseointegration, and minimizing weight for better patient comfort and long-term durability [12]. Hence, the key roles of AI in metal 3D printing processes include design optimization to improve performance, real-time process monitoring and control to maintain quality, and predictive analytics to anticipate mechanical properties and defects, enabling proactive adjustments, which are summarized in this section.

2.1. Design Optimization

Design optimization utilizes AI algorithms to improve structural and functional aspects of printed 3D structures [13]. In biomedical applications, this is critical for implants and prosthetics, where precise mechanical performance, patient-specific fit, and biocompatibility determine clinical success [14]. Computationally guided design optimization and process control are advancing polymer and bioprinting fields, supporting the development of patient-specific metallic implants for orthopedic, cranial, and dental applications [15].

Various AI techniques, such as ML-augmented topology optimization, generative design, and predictive models, have been applied to achieve improvements in design optimization for biomedical applications, which enhance biomedical implant performance [16] as summarized in

Table 1. Comparison of AI methods with their key functions and biomedical applications.

AI Method	Key Function	Example Application
Topology optimization	Optimizing material layout for weight and strength	Lightweight orthopedic [19] implants, dental prosthetics [20]
Generative design	Automated generation of design alternatives	Customized cranial implants [21], bone scaffolds
Machine learning	Predicting mechanical properties and optimizing parameters	Predicting implant fatigue life, optimizing surface roughness

. Topology optimization identifies efficient material layout to minimize weight while also maintaining strength, essential for lightweight orthopedic and dental implants [17]. Generative design algorithms automatically form multiple design alternatives based on performance goals and constraints, enabling rapid, patient-specific exploration of implant geometries [18]. Additionally, machine learning models predict the ability of distinct design parameters to affect mechanical properties, enabling smarter decision-making and rapid iteration. These methods accelerate the development of optimized implant geometries tailored for specific biomedical applications with reduced trial-and-error [7].

2.1.1. Topology Optimization Techniques

Topology optimization identifies material distribution to minimize mass while preserving mechanical performance [22]. In biomedical implants, this approach reduces weight without compromising structural integrity, enhancing patient comfort and surgical outcomes [18]. Finite element analysis (FEA) is widely used in topology optimization algorithms, particularly for evaluating stress, strain, and deformation in solid structures, providing critical insights into mechanical performance and structural reliability [23,24,25]. Peto et al. [19] applied topology optimization to 3D micro-architected structures suitable for implantable devices using metallic structures compatible with Ti-6Al-4V and CoCr alloys. FEA was used to evaluate mechanical reliability and manufacturing constraints, enabling the creation of complex, lightweight geometries tailored to specific load conditions while ensuring manufacturability for metal 3D printing (Figure 1). The study demonstrated up to 25% reduction in component weight without compromising structural integrity and standard yield strength for orthopedic implants. Similarly, Jhunjhuwala et al. [20] used topology optimization with FEA to produce Ti-6Al-4V hip implants fabricated through laser powder bed fusion (LPBF) using 200 W laser power, 1200 mm/s scan speed, 30 µm layer thickness, and 90 µm hatch spacing. Optimized implants showed 15–20% mass reduction, ~12% residual stress reduction, and a compressive yield strength above 850 MPa. These improvements highlight the ability of topology optimization to produce patient-specific implants with enhanced durability and reduced risk of deformation during manufacturing. Zhou et al. [26] introduced a multicomponent topology optimization approach for LPBF-fabricated stainless steel 316L parts, simultaneously optimizing topology, component partition, and build orientation. Their framework incorporated anisotropic tensile strength (~600 MPa along the build direction) and stress concentration analysis, reducing stress hotspots by 18% and improving fatigue resistance under cyclic loading conditions. This integrated approach enhanced the performance and manufacturability of additively manufactured components for providing a mechanically reliable, lightweight implant suitable for load-bearing biomedical applications [26]. In addition to FEA, topology optimization can incorporate other computational analysis methods such as computational fluid dynamics (CFD) for fluid–structure interactions, multibody dynamics for kinematic evaluations, and optimization-based analyses to predict structural responses under various conditions [27,28,29]. These approaches complement FEA by providing a more comprehensive understanding of performance, manufacturability, and robustness in complex implant designs.

2.1.2. Generative Design

Recent studies have highlighted the potential of AI-driven generative design to redefine engineering workflows, enabling more efficient exploration of design spaces and faster iteration of patient-specific implants [23]. Generative design leverages algorithmic approaches, such as topology optimization, to produce multiple design solutions based on defined parameters like weight, strength, and material constraints [30]. While many generative design tools may incorporate topology optimization as part of their workflow, topology optimization and generative designs are distinct: topology optimization is a mathematically robust optimization method, whereas generative design explores multiple candidate solutions using algorithmic strategies. In biomedical additive manufacturing, this enables the production of complex structures that are tedious for manual manufacturing [31]. While generative design may employ AI to rank or evaluate candidate solutions, the structural optimization itself is governed by established mathematical algorithms [31,32,33]. The integration of generative algorithms with human-centered design principles has advanced biomedical product development by enabling AI to translate patient-specific or user-driven constraints, such as CT scans or ergonomic data, into optimized and functional geometries, as shown in Figure 2 [34]. For instance, Voronoi tessellation, which is a computational method that partitions space into regions based on proximity to seed points, is often used to mimic the porous architecture of natural bone [35]. This biomimetic approach improves osseointegration, reduces implant weight, and enhances mechanical performance, critical for orthopedic and cranial implants.

Sharma et al. developed a biomimetic titanium cranial implant by applying Voronoi tessellation to generate a porous structure closely resembling natural trabecular bone [21]. In this study, a patient-specific defect model was created using CT imaging, and implants were manufactured in Grade II titanium via Selective Laser Melting (SLM) with a 60 µm layer thickness, 200 W laser power, and 1200 mm/s scanning speed. Computational algorithms optimized the porous network for osseointegration while maintaining mechanical strength and reducing implant weight [36]. The Voronoi tessellation mimicked the trabecular architecture of natural bone, providing improved load distribution and patient-specific anatomical conformity. Optimized process parameters minimized defects such as residual porosity and microcracks, demonstrating the clinical value of AI-guided generative design in cranial reconstruction. While generative design currently relies primarily on human input and CAD-driven processes [37], these approaches highlight the potential of AI to autonomously optimize patient-specific implant architectures.

2.1.3. Machine Learning (ML)

Machine learning (ML) is increasingly applied to enhance metal additive manufacturing by analyzing complex, high-dimensional process datasets to optimize process control, defect prediction, and improve design quality [38]. Equbal et al. [39] summarized that ML techniques are used across design optimization, real-time process monitoring, and process control. Techniques such as support vector machines (SVM), convolutional neural networks (CNN), and random forests achieved up to 85% prediction accuracy in detecting defects, such as porosity and cracks, using acoustic and thermal sensor data. ML-based parameter optimization reduced experimental trial and error by up to 30%, accelerating the development of patient-specific biomedical implants while ensuring high mechanical precision and structural reliability.

Zhang et al. [40] developed a hybrid ML framework that combined gradient boosting regression with finite element simulations to predict residual stress and distortion in laser powder bed fusion (LPBF) processes. Simulating key process variables, such as laser power, scan speed, and layer thickness in a thermal gradient-enabled environment, allows for residual stress prediction errors below 10%, enabling proactive adjustments to maintain mechanical integrity. Modeling under thermal gradients is particularly important for biomedical implants, as it ensures that the final geometry and structural stability remain robust when exposed to body temperature and local thermal variations, reducing the risk of fractures or deformation during implantation. By minimizing distortions, implants achieved consistent geometries suitable for load-bearing applications while preserving biocompatibility and patient safety. Additionally, Gan et al. [41] introduced universal low-dimensional scaling laws that relate printer parameters to melt pool morphology and defect formation. These dimensionless relationships enhanced the generality of ML models by embedding physical features into data-driven approaches for improving performance across distinct metal alloys and printer platforms. For biomedical implants, this capability ensures reproducible geometries and mechanical properties that withstand physiological loads and promote consistent osseointegration. The combination of ML with physical process understanding provides a safer, more predictable approach to producing patient-specific devices, minimizing risks of mechanical failure or compromised biocompatibility.

2.2. Process Monitoring and Control

Process monitoring and control are critical for ensuring the quality and consistency of metal additive manufacturing processes based on the advancements in ML algorithms [42]. Real-time monitoring systems utilize sensor data, such as thermal imaging, acoustic emissions, and melt pool characteristics, to detect anomalies and maintain optimal printing conditions, as displayed in Figure 3 [43]. Integration of AI-driven analytics with these monitoring tools enabled dynamic adjustment of process parameters, such as laser power (195–225 W) and scan speed (1250–1400 mm/s) in LPBF, for reducing defects and improving their overall reliability, especially in high-precision biomedical applications [44,45]. These analytics can identify defects such as porosity, cracks, and spatter in situ by continuously monitoring the process, which in turn allows for prompt adjustments to printing parameters, such as melt pool temperature control, during laser metal deposition [46]. Closed-loop control systems were identified to possess the ability to dynamically modify laser power, scan speed, and powder feed based on sensor feedback to stabilize the melt pool and minimize variations [47]. These advantages are particularly critical in biomedical applications where precision and material integrity can directly impact implant performance and patient safety [48]. However, challenges remain in effectively fusing heterogeneous sensor data and developing adaptable AI models for diverse materials and printer configurations, where current studies are steered towards complete autonomous and self-correcting metal 3D printing systems [49].

The transformative potential of AI in metal 3D printing approach depends on its ability to integrate across all stages of the manufacturing processes, such as design optimization, process monitoring, and predictive analysis that operate in a continuous feedback loop [5]. For instance, real-time monitoring data that are collected during the synthesis process can be used to refine ML models, which can eventually enhance future design interactions or adjust parameters during the printing process to reduce defects [50]. Predictive insights generated from historical printed data can inform generative design algorithms by narrowing viable design spaces for efficient and manufacturable outcomes [51]. This synergistic loop not only reduced development time but also created a robust and self-modifiable manufacturing pipeline [52]. In biomedical applications, where patient-specific geometries and clinical precision are paramount, this closed-looped machine learning algorithm integration supported the rapid fabrication of safe and effective implants while reducing the reliance on costly trial-and-error prototypes [53].

2.3. Predictive Analysis

Predictive analysis depends on historical and real-time manufacturing data that are combined with ML algorithms to forecast their physicochemical properties and potential defects before the completion of the printing process [7]. These predictive tools enabled proactive optimization of printing strategies by modeling the relationship between process parameters and outcomes such as residual stress, porosity, and microstructural features [54]. For instance, the incorporation of physical features, such as scaling up of print parameters to melt pool behavior, enhances the accuracy and generalizability of these models across distinct materials and machines [55]. In biomedical implant manufacturing processes, these predictive capabilities facilitate early identification of risk factors for structural failure or biocompatibility issues for improving design iterations and reducing costly trial-and-error cycles [56]. Integration of predictive analysis with in-line monitoring systems was developed to form comprehensive combinations of the manufacturing process for improving its precision and reliability in the future [57].

Table 2. Key parameters and outcomes of metal 3D printing in biomedical applications.

Process	Beam Type	Laser/ Electron Power	Scan Speed	Layer Thickness	Powder/ Material	Nanoparticle Additives	Key Outcomes
LPBF/SLM	Laser	195–225 W [44]	1250 mm/s [44]	Not reported	Ti, CoCr, stainless steel [44]	Ag, Ti, Cu [6,7]	Defect reduction, improved precision, and enhanced stability are critical for high-precision biomedical implants [44,45,46,47].
LMD/Melt Pool Control	Laser	Not reported	Not reported	Not reported	Ti alloys [46]	Not reported	Melt pool control, reduced cracks and porosity, supports stable fabrication of load-bearing implants [44,45,46,47].
Predictive Analysis	N/A	N/A	N/A	N/A	N/A	N/A	ML predicts residual stress, porosity, and microstructural features; optimizes printing parameters to improve reproducibility and patient-specific implant performance [49,50,51].

is the summary of key parameters and outcomes of metal 3D printing in biomedical applications

2.4. Limitations and Future Outlook

Despite ongoing progress, several challenges still limit the widespread adoption of AI in metal additive manufacturing, even as they drive further technological advancements [38]. The availability of high-quality, labeled datasets remains limited due to complex data variability and privacy concerns that are notably within specialized biomedical applications [58]. Additionally, variations in machine configuration, sensor technologies, and printing environments make it difficult for AI models to generalize effectively across different systems [59]. Regulatory frameworks for AI-assisted medical device manufacturing processes are still under development, which highlights the need for explainable and auditable AI decision-making frameworks to ensure patient safety and regulatory compliance [60]. Thus, current research is focusing on developing physical AI models that can incorporate fundamental manufacturing principles with federated learning approaches that safeguard proprietary data, and standardized digital twins that are capable of simulating entire manufacturing cycles [61]. Autonomous metal 3D printing systems with real-time correction and optimization capabilities are expected to become integral to precision medicine and the fabrication of customized implants in the future due to the evolution of these technologies [62].

2.5. Biomedical Relevance of Metal Additive Manufacturing Process Using AI

The integration of AI in metal additive manufacturing processes possesses significant promises for advancing patient-specific biomedical solutions [63]. For instance, AI-driven design optimization enabled the formation of lightweight and mechanically robust implants that are tailored to the anatomy and functional requirements of an individual in orthopedic and dental implantology [64]. Real-time monitoring process ensured the consistent fabrication of dense and defect-free structures that are essential for load-bearing applications [65], while predictive analytics help to anticipate fatigue behavior and biocompatibility issues prior to implantation [66]. These capabilities are significant for high-risk patients or complex reconstructions where traditional one-size-fits-all devices may lead to poor integration, discomfort, or early failure [67]. Moreover, closed-loop machine learning algorithm systems can reduce the need for repeated surgical revisions by enhancing manufacturing consistency, which eventually reduces healthcare costs and improves patient outcomes [68]. Hence, the role of AI in enabling safe, effective, and scalable production of the next generation implants will be prominently developed within precision medicine as they continue to mature in the future [69].

3. Nanomaterials in Metal 3D Printing Applications

Nanomaterials are particles or structures on the scale of 1 to 100 nanometers that have emerged as powerful additives to enhance the performance of metal components produced by the metal 3D printing approach [70]. Significant improvements can be achieved in mechanical strength, wear resistance, biocompatibility, and functional properties by integrating nanomaterials into metal matrices for tailored biomedical applications [70]. Additionally, nanomaterials can enable precise control over degradation rates in bioresorbable implants for opening novel possibilities for temporary implant designs [71]. Similarly, nanomaterials are suitable for additive manufacturing processes due to their size compatibility with the microstructural features that are formed during layer-by-layer solidification [72]. Their high surface area-to-volume ratio is significant for enhancing interfacial bonding and reactivity, enabling tailored surface chemistries that improve implant-tissue interactions [73]. This nanoscale control is essential for optimizing both bulk and surface properties that are crucial in biomedical implant applications, as summarized in

Table 3. Summary of nanomaterial-enhanced biodegradable metal implants, nanostructure integration, degradation/mechanical implants, and functional benefits.

Material/System	Nanostructure Approach	Degradation/Mechanical Impact	Benefits
Zn-0.8 Li porous scaffold [77]	SLM and salt leaching for controlled porosity	7–12% weight loss in 28 days	Biodegradable support; enhanced osteointegration
Zn + β-TCP composite [78]	LPBF with ceramic nanoparticle reinforcement	Uniform degradation over 12 weeks	pH buffering; improved osteoconductivity.
Mg-MOF coated surface [79]	MOF coating forms a protective barrier	Significantly reduced corrosion rates	Stimuli-responsive degradation; antimicrobial benefits
Zn-Li-Sr alloy [80]	Alloying with Li and Sr for targeted enhancement	Tensile strength increases over 6 times compared to pure Zn; uniform degradation in diabetic models	Enhanced strength, osteoblast proliferation, and antioxidant response in diabetic bone healing application.

[74]. Conventional metal 3D printing processes often face challenges in delivering the biological performance and degradation control that are required for complex healing environments due to increased demand for patient-specific implants [75]. Nanomaterials bridge this gap by enabling fine-tuned structural, mechanical, and biochemical modifications at the micro- and nanoscale regimes [76]. These enhancements are essential in orthopedic, craniofacial, and dental implants where tissue regeneration, infection prevention, and mechanical durability are simultaneously required [73].

3.1. Mechanical Strength and Wear Resistance

Nanomaterials are widely utilized as reinforcements in metal additive manufacturing methods to enhance mechanical strength and wear resistance, which is a critical factor in high-performance biomedical implants [81]. Nanoparticles such as carbon nanotubes (CNTs), graphene, and ceramic nanoparticles (alumina (Al₂O₃), titanium carbide (TiC)) were identified to contribute to grain refinement, pinning of dislocations, and strengthening of load transfer [82]. These mechanisms collectively have improved the tensile strength, hardness, and fatigue resistance of metal 3D-printed components [83]. Rahman et al. [84] integrated nanomaterials into metal matrices for significantly enhancing their structural properties by refining microstructures during solidification and for reducing defects such as porosity and interlayer delamination. The authors highlighted that metal matrix nanocomposites (MMNCs) reinforced with nanoparticles can exhibit improved tribological properties, including reduced wear rates and enhanced toughness. The study also summarized the effects of incorporating nanomaterials into various metal matrices and reported that aluminum-titanium carbide (Al-TiC) nanocomposites produced via the selective laser melting (SLM) process can increase 48% of hardness and a reduction of 36% in wear rate, compared to unreinforced aluminum. These enhancements were attributed to uniform TiC dispersion, strengthening of the grain boundary, and improved densification. Similarly, CNT-reinforced stainless-steel matrices showed increased tensile strength of up to 40% with improved load distribution and crack-bridging mechanisms. These reinforced CNTs were identified to be advantageous in load-bearing implants such as femoral stems and spinal cages, where long-term durability under cyclic stress is essential [85]. Further, these mechanical improvements are crucial factors for implants such as hip stems, cranial plates, and spinal cages, as they have to handle a large load weight [86]. Rouf et al. [87] demonstrated that titanium-based implants reinforced with hydroxyapatite (nHA) and graphene nanoplatelets can exhibit a wear volume reduction of over 50% under simulated loading of the joint, as shown in Figure 4. Additionally, the reinforcement of implants with nanostructured surfaces showed finer control over surface roughness (Ra < 0.5 µm), which is beneficial for reducing micromotion and promoting bone integration. These enhancements extended the lifespan of implants and reduced their failure risks, especially in high-stress anatomical regions. Hence, the incorporation of nanomaterials improved the bulk mechanical properties of metal 3D printed parts and also enabled tunable surface characteristics. These dual benefits make nanocomposite-enhanced metal AM a promising strategy for the fabrication of durable, long-lasting biomedical implants [88].

3.2. Biocompatibility and Antimicrobial Properties

Biocompatibility and resistance to microbial colonization are essential in biomedical metal additive manufacturing processes, similar to mechanical strength [89]. Nanomaterials can enhance these properties through improved surface chemistry, functional coatings, and tailored micro/nano-topographies that influence cell behavior and bacterial adhesion [90]. Metal implants modified with nanoparticles such as hydroxyapatite (nHA), silver (Ag), zinc oxide (ZnO), and titanium dioxide (TiO₂) were identified to exhibit enhanced biointegration while simultaneously reducing infection risks [82]. One of the major clinical challenges for implanted biomedical devices is postoperative infections, which are often caused by pathogens such as Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa [91]. These bacteria can form biofilms on implant surfaces that are resistant to antibiotics and host immune responses [92]. Infections commonly result in pain, swelling, loosening of the implant, prolonged healing, or, in severe cases, implant failure and revision surgery [93]. In particular, S. aureus is responsible for about 50–60% of orthopedic implant-associated infections, which lead to osteomyelitis and soft tissue abscess formation [94]. Osseointegration is the direct structural and functional connection between bone and the surface of a load-bearing implant, which is critical for implant stability and long-term clinical success [95]. Enhancement of osseointegration through surface modifications in the nanosize regime can significantly improve implant longevity and reduce the risk of complications [96].

Rouf et al. [87] demonstrated that integration of nano hydroxyapatite (nHA, 5 wt%) into titanium alloy structures can significantly increase human osteoblast cell viability by over 30% after 72 h in vitro compared to standalone Ti-6Al-4V. The nanoscale roughness (Ra ≈ 0.42 µm) induced by nHA has promoted protein adsorption and cellular adhesion, which indicates the presence of osseointegration. These surface-modified implants also showed wettability (contact angle < 60°), which is strongly associated with enhanced bone integration. Further, silver nanoparticles (AgNPs) have been widely investigated for antimicrobial performance in addition to the promotion of osseointegration [97]. Clarissa et al. [98] reported that TiO₂/Ag nanocomposite coating applied to 3D printed Ti implants can lead to 95% of bacterial reduction against Staphylococcus aureus and Escherichia coli in 24 h cultures. This effect was primarily due to Ag⁺ ion release and reactive oxygen species (ROS) generation, which compromised bacterial membranes without affecting mammalian cell function. These coatings also maintained high cytocompatibility with high fibroblast viability of 90%, which is an indication of selective toxicity.

3.3. Controlled Degradation

The degradation rate control of bioresorbable metal implants is critical to ensure their safe and effective performance during tissue healing and regeneration [99]. Nanomaterials have emerged as powerful modifiers to fine tune the corrosion behaviors and degradation kinetics of these implants, where magnesium and zinc-based alloys were widely studied for biomedical applications [100,101]. It is noteworthy that magnesium (Mg), zinc (Zn), and iron (Fe) are the most commonly studied biodegradable metals [102]. Each of these metals has unique degradation characteristics; Mg alloys possess rapid corrosion rates that are ideal for short-term support, while Zn and Fe exhibit slow degradation, making them suitable for long-term applications [103]. Precise control remains a challenge due to factors such as localized corrosion, mechanical failure, and gas formation [104]. The integration of nanoparticles or alloy elements has been shown to refine grain structures for enhancing corrosion uniformity and stabilizing degradation behavior [105]. Implants must also degrade in harmony with the healing timeline of the body, while enhancement of osseointegration and antimicrobial resistance can ensure biological compatibility [106]. Uncontrolled degradation can compromise mechanical integrity or form toxic byproducts, which highlights the need for precision engineering at the nanoscale [107]. Thus, degradation control is a challenge in material fabrication and also a biological imperative, especially in temporary scaffolds and resorbable implants [108].

A recent study by Alli et al. [77] demonstrated the ability of additive manufacturing (AM) strategies such as selective laser melting (SLM) combined with a salt leaching approach in producing zinc-lithium scaffolds (Zn-0.8Li) with controlled porosity. These scaffolds exhibited a 7–12% mass loss over 28 days under conditions to improve degradation rate for temporary bone support while avoiding harmful accumulation of corrosion byproducts. Their interconnected porous architecture also promoted osteoblast attachment and proliferation for supporting both mechanical and biological performance. Further, Liang et al. [78] emphasized multiple strategies to highlight the recent advancements in 3D printing of biodegradable metals for orthopedic applications and optimize the degradation behavior, especially highlighting the relationship between porosity, alloys, and surface nanostructures. They revealed that the addition of biodegradable nano-ceramics such as nano-hydroxyapatite (nHA) or graphene oxide (GO) towards metal matrices can help in improving the osteoconductivity and tailor the rate of ion release for directly influencing degradation kinetics. Moreover, the study also highlighted emerging techniques such as gradient porosity design and functionally graded coatings, which lead to region-specific degradation control, that are essential for complex applications such as orthopedic fixation plates or cranial plates.

Recent nanocoating strategies, such as the use of metal–organic frameworks (MOFs), have shown great promise, apart from physical and chemical strategies [109]. Fattah-alhosseini et al. [79] demonstrated that MOF coatings applied to magnesium alloys form porous and protective barrier layers that significantly reduce corrosion rates both in vitro and in vivo. These MOF coatings are designed to react towards physiological stimuli by releasing ions and controlling local degradation, while enhancing surface biocompatibility and providing antimicrobial benefits. This multifunctional approach further expanded on the design space for bioresorbable implants. In recent times, Rao et al. [80] introduced a Zn-Li-Sr ternary alloy that is designed for diabetic bone environments. This alloy achieved uniform degradation and also demonstrated a six-fold increase in tensile strength compared to traditional Zn alloys. In vivo testing in diabetic models showed significantly improved osteoblast proliferation, bone ingrowth, and antioxidant responses for showcasing a major step forward in designing alloys that are suitable for compromised healing conditions.

Despite these advantages, future developments in the metal additive manufacturing process with nanomaterials must overcome challenges in material uniformity, biocompatibility, and scalable production [110]. The integration of AI and predictive modeling methods showed great promise for enabling precise control over fabrication methods and is fine-tuned to fabricate patient-specific implants [111]. The next generation of nanostructured implants can revolutionize personalized medicine by enhancing degradation profiles, improving bioactivity, and minimizing infection risks in the future [112].

4. AI-Guided Material Discovery and Process Optimization

Optimization of nanoparticles’ integration and manufacture remains a challenge, even though they offer remarkable improvements [113]. AI plays a transformative role in accelerating material discovery by refining fabrication parameters and minimizing costly reliance on trial-and-error experimentation due to advantages in metal AM processes [114]. Traditional development of metal matrix nanocomposites (MMNCs) often requires numerous iterations to identify the optimal composition and processing parameters, as an approach that is time-consuming, resource-intensive, and limited in scope [115]. It is possible for AI to enable a data-driven framework for allowing researchers to rapidly screen material candidates, predict performance outcomes, and dynamically adjust processing parameters in real time [116]. These capabilities are particularly valuable in biomedical applications, where implants must meet stringent requirements for mechanical strength, biocompatibility, and controlled degradation [117]. Hence, the capability of AI technologies, from machine learning and generative models to digital twins and physical networks that are revolutionizing the design and optimization of nanomaterial-enhanced metal 3D printing systems, has been summarized in this section and

Table 4. Summary of AI models used in nanomaterial-enhanced metal AM 3D printing processes.

Aspect	Details	References
Identification of new MMNCs	AI models predict mechanical properties based on nanoparticle type, size, and dispersion, and optimize loading to balance strength and risk of agglomeration.	[118,119,120]
Optimization of sintering parameters	Physics-informed models predict melt pool dynamics and temperature gradients; adaptive control reduces defects and residual stresses.	[121,122,123]
Real-time monitoring and adaptive control	Deep learning models analyze melt pool images, dynamically adjust laser power/scan speed, reduce porosity, and improve accuracy.	[124,125]
Reducing trial-and-error experimentation	Digital twins and transfer learning enable the virtual replication and reuse of models, reducing the need for experimental iterations.	[126,127,128,129]

.

4.1. Identification of Novel Metal Matrix Nanocomposites

AI and ML have revolutionized the discovery and design of novel metal matrix nanocomposites (MMNCs) by enabling accelerated material innovation through the prediction of optimal properties and processing conditions without exhaustive experimental trials [130]. These data-driven approaches significantly reduce the trial-and-error experimentation in traditional material development and help to identify the nanomaterial reinforcements for enhancing mechanical, thermal, and biological performance [131]. Yu et al. [118] provided a comprehensive framework for applying AI techniques to nanocomposite design to focus on the supervised learning algorithms for improving nanoparticle type, size, and dispersion quality, as well as their mechanical performance. The study highlighted case studies where AI models are able to accurately predict the tensile strength and toughness improvement in aluminum and titanium matrices that are reinforced with ceramic nanoparticles, such as SiC and TiC. Notably, the study emphasized that optimizing the loading of nanoparticles involves balancing enhanced load transfer mechanisms against risks of particle agglomeration and processing challenges. These nuances support the targeted design of MMNCs for biomedical implants that are required for both mechanical durability and biocompatibility. Their approach also includes feature selection techniques that identify the impactful nanoparticle characteristics for specific properties by streamlining experimental efforts.

Wu et al. [119] introduced a machine learning framework for predicting the mechanical behavior of novel MMNCs based on input features such as nanoparticle type, volume, fraction, and processing parameters. The model was trained to estimate yield strength and ductility across distinct matrix-nanoparticle systems using a dataset of over 150 experimental entries. In this study, a multi-objective optimization strategy was implemented using Pareto front analysis to identify optimal trade-offs between mechanical properties. These Pareto fronts represent a set of optimal solutions where improving one property (such as hardness) would lead to tradeoffs in another (ductility), helping researchers to identify the best balance among competing performance criteria. This method significantly reduced the time and cost of discovering MMNC compositions that are suitable for biomedical applications such as load-bearing implants, where both strength and ductility are critical. Further, Guercio et al. [120] focused on the integration of AI-guided material discovery with process optimization in metal AM processes. They highlighted recent advantages, such as the ability of AI models to identify novel nanoparticle reinforcements and simultaneously optimize laser parameters, powder characteristics, and post-processing treatments to achieve defect-free and homogenous microstructures, as shown in Figure 5. The study emphasized that the coupling of reinforcement discovery with parameter tuning is crucial to avoid issues such as nanoparticle clustering, incomplete melting, or residual stresses that degrade implant performance. Case studies include AI-designed TiC and graphene nanoplatelet reinforcements in titanium matrices produced via selective laser melting (SLM), which achieve improvements in hardness and corrosion resistance while maintaining favorable surface roughness for osseointegration. Their findings suggested that integrated AI frameworks can reduce experimental iterations by over 60%, significantly accelerating development timelines for biomedical implants. Thus, these studies demonstrated the ability of AI-driven predictive modeling and optimization in reshaping MMNC development for biomedical AM processes. Researchers can identify nanomaterial compositions and processing conditions that meet stringent mechanical, biological, and functional requirements by leveraging large datasets, multi-objective algorithms, and hybrid machine learning models [132]. This data-guided approach has improved implant reliability, safety and also expedited innovation cycles for supporting the growing demand of patient-specific, high-performance biomedical devices [133].

4.2. Optimization of Sintering and Laser Parameters

The quality of MMNCs is heavily influenced by material composition and key process parameters such as laser power, scanning speed, hatch spacing, and layer thickness in the metal AM process [134]. These factors control the thermal history of the process, influence melt pool behavior, microstructural evolution, porosity, and residual stress [135]. Improper settings can result in defects such as ball, keyhole, and crack formation, which undermine the mechanical integrity and biological performance of the implants [136]. Thus, AI, especially physical neural networks (PINNs), surrogate models, and reinforcement learning algorithms, are identified to be beneficial for optimizing the parameters to overcome the limitations of improper settings in AM processes [8]. AI allowed researchers to minimize experimental iterations and rapidly discover optimal process windows that ensure consistency, mechanical robustness, and biocompatibility in printed structures by integrating scientific knowledge with data-driven techniques [137]. The ability of AI to facilitate both predictive modeling and real-time process control of AM in MMNCs is explored in this section.

4.2.1. Modeling of Physical Processes

Physical AI models integrate equations of heat transfer, fluid dynamics, and material behavior into machine learning frameworks. These models significantly improve prediction accuracy, even with limited training data, making them ideal for the complex thermal environments of AM [138]. Zhu et al. [121] developed a physical neural network (PINN) that forms a computational model of melt pool dynamics in a laser powder bed fusion (LPBF) process. The resultant PINN model was able to predict temperature gradients, melt pool dimensions, and cooling rates with high accuracy, even in data-scarce conditions, by incorporating conservation laws of energy and mass. The model achieved 90% of match compared to the experimental melt pool of shapes, which highlights its effectiveness for tuning laser parameters in titanium-based systems. This level of predictive fidelity is valuable in implant fabrication, where uniform thermal profiles are crucial to avoid residual stresses and microstructural inconsistencies that could compromise long-term function. Similarly, Jiang et al. [122] introduced a hybrid model by combining physical simulations with machine learning algorithms to predict melt pool temperature and shape. Their model was trained on a limited dataset of scan speeds and laser powers, and validated predictions of melt-pool width and depth, resulting in a significant reduction in computational cost compared to full-scale finite element simulations. This enabled rapid iteration in selecting energy densities that optimize fusion without overmelting, especially in materials such as magnesium alloys used for biodegradable implants. Likewise, Ajenifujah et al. [123] proposed a dual-surrogate modeling approach that includes a baseline model and a correction network to predict thermal behavior in complex geometries. The framework allowed rapid estimation of localized heat accumulation and thermal history during sintering for curved surface or internal lattice structures that are commonly identified in orthopedic and craniofacial implants. These physical models are beneficial for better understanding and control of microstructural outcomes, such as grain refinement, phase distribution, and porosity, which are key components in ensuring mechanical stability and osseointegration of medical implants.

4.2.2. Real-Time Monitoring and Adaptive Control

AI is also used for real-time process monitoring and adaptive control during metal 3D printing processes in addition to predictive models [139]. Deep learning architectures can detect defect formation in real time by analyzing thermal or optical imaging data and adjust laser parameters accordingly to maintain optimal printing conditions [42]. Ogoke et al. [124] developed a deep reinforcement learning (DRL) framework that can continuously regulate laser power and scan speed based on thermal feedback during laser powder bed fusion (LPBF), as shown in Figure 6. Their system learned to optimize pool geometry across varying geometries for reducing porosity by over 40% and improving dimensional accuracy by 25%. The DRL framework significantly minimized overheating and maintained consistent interlayer bond formation in intricate biomedical structures such as porous scaffolds and cranial implants. Furthermore, Hemmasian et al. [125] introduced a convolutional neural network (CNN), a type of deep learning model particularly effective at processing image data, to analyze in situ melt images and predict optimal laser power and scanning speed combinations for titanium-based MMNCs. The CNN model was trained to detect early signs of ball and keyhole formations, which are considered common defects during SLM. This model was integrated into a feedback loop for adaptive process control. The researchers have achieved a 45% reduction in surface porosity and a 20% improvement in yield strength compared to baseline prints with this AI-assisted approach. This real-time predictive monitoring framework offered significant advantages for the fabrication of dense and mechanically robust implants with minimal structural flaws, especially in biomedical applications where surface integrity and strength are critical. AI-guided adaptive systems represent a shift from generalized parameter sets to a self-regulated manufacturing process [140]. This ensured the maintenance of essential structural elements and biocompatibility of the print, even in highly customized or geometrically complex designs [141]. Further, AI-driven strategies are reshaping the optimization of sintering and laser parameters in metal AM processes by combining physical modeling with real-time monitoring and control [114]. These approaches reduced porosity as well as defects, and ensured uniform microstructures and thermal stability, thereby enhancing the mechanical performance and biological compatibility of the implants [142]. Hence, the integration of AI into metal AM workflows will play a vital role in the scalable production of reliable and patient-specific biomedical devices, as these AI models are predicted to advance in the future.

4.3. Reducing Trial and Error Experimentation

Development and optimization of MMNCs in AM remain highly dependent on empirical trial-and-error methods to identify ideal process parameters, such as scan speed, laser power, hatch spacing, and powder composition. These iterative experimental cycles are costly, time-consuming, and limited by reproducibility, especially for biomedical-grade materials, where mechanical, structural, and biocompatibility constraints must be precisely balanced [55]. AI-driven strategies, such as digital twins, transfer learning, and generative optimization frameworks, are emerging as transformative solutions to accelerate the discovery process, reduce redundant trials, and enhance design accuracy for next-generation implants [143].

4.3.1. Digital Twins for Closed-Loop Process Prediction

Digital twins create real-time virtual replicas of physical AM processes by combining computational simulations, sensor feedback, and AI-based predictive models [144]. These systems allow continuous comparison between predicted and actual outcomes to dynamically adjust process parameters. Liu et al. [126] introduced a deep neural operator-based digital twin designed for laser powder bed fusion (LPBF). This model combined physical surrogates with Fourier neural operators to predict melt behavior and microstructural outcomes. The model enabled real-time parameter adjustments to minimize defects and predict future states for reducing experimental iteration counts by over 50% after coupling them with sensor data. Similarly, Chen et al. [127] utilized laser-directed energy deposition (L-DED) by integrating multisensory data (thermal, acoustic, visual) into a digital twin for in situ defect detection and robotic toolpath correction, enabling autonomous adjustment during fabrication. The system successfully identified pore formation and deposition anomalies for real-time corrections without interrupting production. These tools replicate the behavior of the physical environment, enabling rapid exploration of processing strategies without destructive tests, which is crucial for designing patient-specific implants or materials accurately and efficiently. These digital twins, by minimizing destructive testing and parameter re-optimization, enable closed-loop manufacturing systems to reduce experimentation while enhancing process reliability and reproducibility, which are critical for medical device certification [145]. Future implementations integrating nanomaterial-AI feedback could allow virtual tuning of nanoparticle dispersion and interface stability before a physical printing approach.

4.3.2. Transfer Learning and Generative Optimization

Transfer learning accelerated model development via knowledge from previously trained neural networks to predict properties of novel materials or geometries with minimal data [146]. Luo et al. [128] employed a transfer learning approach on a convolutional neural network (CNN), that are trained to predict tensile strength and elongation of Ti-6Al-4V based on photodiode sensor signals. The pretrained model was fine-tuned based on new process conditions with minimum samples (20) for achieving 0.89 of R² for ultimate tensile strength prediction and 0.96 of R² for elongation within the range of 900–1150 MPa and 0–17%, respectively. Similarly, Shang et al. [129] used a deep learning and generic algorithm framework to inversely design microstructures for Ti-6Al-4AV for tailoring yield strength and modulus with efficient optimization as illustrated in Figure 7. This method bypassed traditional DOE cycles and expedited matching target properties across material systems. These approaches dramatically reduced the data requirements for new alloy systems and composite formulations, which is crucial for biomedical-grade MMNCs, where experiments are limited by material cost and ethical considerations [111]. When integrated with generative AI, transfer learning can optimize multiple performance metrics simultaneously, such as mechanical strength, corrosion resistance, and biocompatibility, for rapid iteration of functional implants

AI-driven technologies are rapidly redefining the development of MMNCs for biomedical applications [147]. AI-enabled unprecedented precision, speed, and adaptability play a crucial role across the entire design-to-production pipeline, from the intelligent selection of nanoparticle reinforcements to the optimization of laser and sintering parameters, as well as the elimination of costly trial-and-error experimentation [148]. These innovations are critical for medical implants, where mechanical integrity, biocompatibility, and patient-specific customization are essential [145]. Thus, researchers could not meet the complex requirements of physical modeling, real-time monitoring, and generative optimization using conventional methods [32]; however, ML integration has been identified as a means to reduce iterations, costs, and shorten the time to market [149]. In the future, hybrid frameworks combining digital twins and transfer learning could continuously refine predictive accuracy using real manufacturing data, creating a self-improving ecosystem for intelligent AM. This convergence promises to eliminate much of the experimental redundancy currently hindering biomedical 3D printing and accelerate the transition from laboratory validation to clinical-grade production [150].

5. Biomedical Application of Metal 3D Printing with AI and Nanomaterials Integration

AI-integrated nanomaterial formation was utilized to enhance the metal 3D printing process, especially for biomedical applications, such as the fabrication of cranial prosthetics, orthopedic, and dental implants. Recently, Ortis et al. [151] summarized the advantages of developing orthopedic implants using synergistic AM processes, Computer-Aided Design (CAD) technology, thermoplastics, and reverse engineering approaches. The study revealed that the combination of AI and CAD in AM processes can lead to the formation of customized implants with specific porosity for reducing periprosthetic joint infections and surgical site infections by enhancing osseointegration and incorporation of antimicrobial agents [151]. Furthermore, Zhang et al. [152] utilized AI and a 3D printing process for the development of a complex orthopedic plate with nanofibers to improve their thermomechanical properties. In this study, a poly lactic acid (PLA)-based 3D printed plate, which is coated with polycaprolactone–akermanite nanofibers of size around ~253 nm. The results revealed that the maximum pressure force and bending flexural force were enhanced by 4.72% to 16.83% and 21.06% to 21.39%, respectively, after the incorporation of nanofibers. Later, the Levenberg–Marquardt (ML) algorithm for curve fitting method with R² of 0.99 was used to optimize and predict the thermomechanical results in simulated body fluid [152].

Kashwani et al. [153] predicted that integration of metaverse, AI, augmented/virtual reality, teledentistry, CAD, 3D printing, blockchain, and CRISPR (clustered regularly interspaced short palindromic repeats) is beneficial for the future of dental care [153]. Further, Zarei and Farazin [154] recently demonstrated that the synergy of AM processes and ML algorithms can be beneficial for advanced hydroxyapatite scaffold design to be useful in bone regeneration applications. The study summarized that fused deposition modeling (FDM) and direct ink writing (DIW) methods are extensively utilized for the fabrication of hydroxyapatite scaffolds in healthcare applications. Further, incorporation of metals in hydroxyapatite-based scaffolds via a 3D printing process has been identified to be beneficial for improving their efficacy. However, the limitations of these scaffolds, while their fabrication via AM processes, were predicted to be solved by ML models [154]. Furthermore, Dai et al. [155] modified mesoporous arginine-loaded silica nanoparticles with a 3D-printed nanocomposite denture base resin to enhance their mechanical and antimicrobial properties. The study revealed that the incorporation of 3D printed nanocomposite has enhanced the surface roughness, color alteration, hardness, flexural strength/modulus, and antimicrobial efficacy of the denture resin against Candida albicans and Streptococcus mutans, compared to unmodified resin. It has been estimated that the incorporation of an AI tool for the automation of the nanocomposite synthesis process might have further improved the antimicrobial efficacy of the resin [155]. Moreover, Tripathi et al. [63] revealed that the synergistic use of AM, advanced materials, and AI can transform surgical planning and procedures, especially for orthopedic applications, including cranial prosthetics. The authors emphasized the dimensional accuracy of 3D printed models and the general guidelines of FDA (United States Food and Drug Administration) approved anatomical models in designing efficient orthopedic implants or cranial prosthetics [63] as shown in Figure 8. Additionally, Santis et al. [156] designed a novel 3D hybrid structure using AM processes for cranioplasty application. In this study, a porous polyester structure with poly (methyl methacrylate) was incorporated with copper-doped tricalcium phosphate particles to form a modified bone cement. The results showed that thermal resistance was significantly higher in the complex hybrid material than in the standalone polymer. The preliminary theoretical model of the entire head and finite elemental analysis showed that a rigid sphere is responsible for the high thermal resistance of the head implant region [156]. These studies collectively demonstrate that the integration of artificial intelligence, additive manufacturing, and nanomaterials significantly enhances the design precision, mechanical performance, and antimicrobial potential of implants for orthopedic and dental applications. However, it is important to note that most of these findings are currently based on in vitro or computational models, with limited or no in vivo or clinical validation. Therefore, future research should prioritize in vivo animal studies and pilot clinical evaluations to assess the biocompatibility, osseointegration, and long-term safety of these AI-assisted nanocomposite systems to ensure their successful translation from laboratory to clinical practice.

6. Challenges and Future Perspectives

Several challenges remain in AM processes for completely harnessing the potential of nanomaterials in metal 3D printing approaches for biomedical applications, despite their significant progress. One major limitation is in the achievement of uniform nanoparticle dispersion within metal matrices, as agglomeration can lead to defects and inconsistent mechanical properties [157]. Moreover, controlling nanoparticle–matrix interfaces at the atomic scale is essential to optimize load transfer and corrosion resistance, which is difficult due to current manufacturing techniques [158]. Biological concerns also persist, including potential cytotoxicity of certain nanomaterials and long-term in vivo stability of nano-modified implants [159]. Thus, current research focuses on developing advanced surface functionalization methods and biocompatible coatings that can mitigate adverse responses. Additionally, integration of AI and ML algorithms in AM processes has exhibited promising results for optimizing process parameters and predicting nanomaterial behavior to enable precise control over implant properties [56]. Future efforts are expected to emphasize scalable fabrication methods, multi-material printing, and smart implants that are capable of real-time adaptation, for next-generation patient-specific biomedical devices [70]. Further, development of high-quality, multimodal datasets that combine imaging, process parameters, and biological outcomes is crucial to train reliable and generalizable AI models. It has been summarized by Harale et al. [160] that there are three main challenges, such as material limitations, quality assurance, and regulatory compliance, while utilizing 3D printing approaches. The authors emphasized that it is tedious to replicate biomedical properties of natural bone, such as flexibility and strength, while inconsistencies in the parameters of the printing process, environmental conditions, or material quality can lead to defects. Moreover, the study also revealed that it is difficult to meet international medical standards via 3D printing for the fabrication of highly performable, safe, and efficient implants or prosthetics. The study also summarized that the incorporation of AI can be useful in overcoming these limitations of 3D printing processes. However, it has been identified that the incorporation of AI also possesses certain limitations, such as data privacy, algorithm reliability, and regulatory approval. Several recent investigations reported that it is tedious to ensure secure handling and storage of sensitive patient data in compliance with regulatory bodies, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), while utilizing AI in 3D printing processes. Furthermore, variations in data quality, imaging quality, and demographics were also identified to affect the accuracy and performance of AI, whereas AI systems were recognized to require rigorous validation before clinical use, which is considered time-consuming and costly [160].

Recently, Tripathi et al. [63] predicted the future of AM processes, including 3D printing, in medical surgeries, as shown in Figure 9. The authors showed that AM-based 3D printed models can be used to extend the surgical function, reduce threshold, combine concepts, and in tissue engineering applications by adding certain modifications or improvements in AM processes in the future. It is noteworthy that 4D printing processes, metamaterials, and integration of soft robotics, high-speed, and high-resolution printing processes are required to shift the function of conventional static AM processes in surgery into dynamic processes. Further, fabrication of low-cost material or metamaterials with a simple method, novel and miniaturization of 3D printed devices, as well as maintaining a balance between properties and cost, are required to transform AM approaches from small to large scale for medical surgeries [63]. The integration of metals in the form of nanoparticles and AI with real-time adaptive printing systems, federated learning, and digital twinning frameworks was identified to reduce the shortcomings of conventional 3D printing approaches for biomedical applications [75]. However, the utilization of AI in 3D printing processes is still in its infancy, where data limitations, generalizability, regulatory, and ethical hurdles are the limitations that hinder their large-scale biomedical application. It can be noted that the performance of AI can be reduced due to data limitations, as accurate prediction models for implant design and biocompatibility require high-quality, large datasets, which are usually unstandardized or scarce [75]. Similarly, the generalizability of AI algorithms across patient-specific physiological and anatomical variations also limits their effectiveness in personalizing implant design [161]. Likewise, compliance with ethical and regulatory conditions reduces rapid clinical translation of AI-mediated and nanomaterial-enhanced 3D printing approaches, as they have to pass rigorous safety validation, which current regulatory frameworks are not completely prepared to handle [162]. The integration of nanomaterials can lead to toxicity, especially when the nanoparticles are degraded from the printed implant or prosthetic, and cause adverse biological responses [163]. Furthermore, complex multi-material printing remains a challenge, as precise spatial distribution control and nanomaterials interaction within the 3D matrix is still underdeveloped [164]. Thus, synergistic integration of AI, nanomaterials, and enhanced AM processes can be beneficial for improving the efficiency of 3D printed materials for biomedical applications in the future.

7. Conclusions

The integration of artificial intelligence and nanomaterials in metal 3D printing represents a transformative leap in the development of next-generation biomedical implants. AI-driven algorithms offer unprecedented capabilities in optimizing design parameters, predicting material behavior, and enabling real-time quality control, while nanomaterials contribute to enhanced mechanical strength, biocompatibility, and functionality. Together, they enable the fabrication of personalized, high-performance implants tailored to individual patient needs. However, several challenges remain, including the need for large, high-quality datasets, improved standardization across platforms, and a clearer regulatory framework to support clinical translation. Emerging approaches such as federated learning, digital twins, and explainable AI present promising solutions to address these limitations. As the field advances, interdisciplinary collaboration between materials scientists, biomedical engineers, AI experts, and clinicians will be critical to fully harness the potential of AI and nanomaterials in revolutionizing additive manufacturing for healthcare applications.