Real-time mapping of small forces with micrometer resolution is essential for studying soft and biological matter. However, existing techniques are slow, limited in spatial sampling or require non-planar substrates that can perturb cell behavior. Here we present silicon sensor arrays for rapid surface force mapping that operate using the elasto-optically induced wavelength shift in thin polymer-cladded optical ring resonators. Using a nano-indenter, we demonstrate that the sensor array reaches a force resolution down to 12 µN and shows a linear response. We present both a five-ring linear array and a 10×5 two-dimensional array at 15 µm pitch, and demonstrate the feasibility of localization and force mapping of a spherical nanoindentation tip. Combined measurement of forces by nano-indenter and the optical ring resonator sensor presents a methodology for calibrating this type of photonic force sensor. Moreover, good correspondence between measurements and finite element simulations provides evidence for the proposed operation mechanism. The shown combination of biocompatible claddings, strong opto-mechanical coupling, and foundry-ready photonics, presents a route towards scalable, real-time force mapping for soft-matter metrology, tactile interfaces, and in vitro mechanobiology.

The development of techniques to culture and differentiate adult and pluripotent stem cells into diverse cell types over the past decades has sparked an increasing interest in the use of cells for organ regeneration. Such therapies aim to replace lost or damaged cells with functional ones. This can be achieved either through tissue engraftment of therapeutic cells or via their paracrine effects on resident cells, thereby offering a potential cure for debilitating degenerative diseases. The development of regenerative cell therapies, however, is ultimately complex. Effective cell therapy requires the delivery and successful engraftment of therapeutic cells to the correct location or sufficient paracrine activity, while ensuring safety is key to gaining support from funders, caregivers, and patients. A wide variety of cell sources has been used for the development of regenerative cell therapies, ranging from mesenchymal stromal cells (MSC) that act to stimulate local progenitor cells through their secretome to tissue-specific cell types differentiated from adult or pluripotent stem cells and organoids that engraft in tissues. While cell administration to patients is challenging based on both practical and ethical perspectives, the development of techniques to preserve transplant organs ex situ on machine perfusion devices offers a unique opportunity for studying regenerative cell therapy for organ repair in a safe and controllable environment. The present review addresses the current progress of cell therapy for organ regeneration of the intestine, kidney, liver, lung, and heart and discusses the challenges and opportunities of this potentially curing therapeutic approach.

A bstract This study was the first attempt to design, fabricate, and evaluate multi-material bone scaffolds composed of Ti6Al4V and akermanite (Ca₂Mg(Si₂O₇), Ak), produced via direct ink writing (DIW), followed by sintering. Two scaffold architectures were developed (i.e., monolithic and core-shell) aimed at combining the mechanical strength of the Ti alloy with the osteoinductive properties of Ak. Uniaxial compression testing demonstrated that the core-shell scaffolds exhibited higher relative-density-normalized elastic moduli (up to 7.65 ± 0.35 GPa) and yield strengths (up to 444.7 ± 8.1 MPa) than the monolithic designs, namely Ti6Al4V-only scaffolds (elastic modulus: 4.29 ± 0.18 GPa; yield strength: 230.9 ± 1.7 MPa) and 90% Ti6Al4V/10% Ak composite scaffolds (elastic modulus: 3.05 ± 0.08 GPa; yield strength: 24.7 ± 1.4 MPa).The enhanced mechanical performance was attributed to interfacial reinforcement and optimized material distribution. Bioactivity assays in r-SBF revealed surface Ca–P deposition on akermanite-containing scaffolds by SEM and EDS, a response not observed on Ti6Al4V only specimens. Complementary ICP-OES showed marked depletion of phosphate and calcium ions, consistent with rapid HAp nucleation and growth, and substantial silicon release in composite samples, a known pro-osteogenic stimulus. Cell culture assays further confirmed the cytocompatibility of the Ti6Al4V, composite and core-shell scaffolds for preosteoblasts. Furthermore, SEM imaging showed that all the scaffolds supported cell attachment and evidenced a distinct cell spatial distribution depending on scaffold composition and architecture. These results contribute to advancing the scaffold design for bone repair and regeneration by proposing DIW-fabricated Ti6Al7V/Ak core-shell scaffolds that show potential as customizable, load-bearing implants with improved mechanical properties and surface bioactivity relative to the Ti6Al4V scaffolds.

Medical image analysis often involves time-consuming annotation processes. Pediatric image analysis introduces additional complexity due to the scarcity of data, noise, and growth-related anatomical variations, particularly in bone analysis, where bone structures evolve more slowly compared to other organs. This study aims to develop a segmentation model that scales across different age groups, reduces annotation effort, and ensures high accuracy, particularly in low-quality images. To address these challenges, we propose a segmentation pipeline (PedVision) that first uses a Region of Interest (ROI) network to identify relevant regions, followed by a foundation model that translates each region into meaningful instances. These instances are then mapped to segmentation classes through an instance classifier (IC) network. To initiate rounds of the training of ROI and IC networks, we developed a fast, semi-automated annotation framework that leverages foundation models to annotate a subset of images using an object-level approach. In subsequent rounds, a human discriminator selects promising predictions from the last round, which are fed by unseen data, progressively enriching the model's training dataset for further fine-tuning of the networks. The networks are expanded from low-parameter to high-parameter models across rounds, incorporating a curriculum learning approach to capture increasingly complex features. We evaluated PedVision on 552 hand X-ray images of children, retrieved from the publicly available Radiological Society of North America (RSNA) and Digital Hand Atlas (DHA) datasets, which represent a diverse range of ages and racial backgrounds. PedVision performed segmentation of 19 hand bones, grouped in five classes, and was compared against U-Net and DeepLabV3+ models using ResNet34 and ResNet101 backbones, as well as the SegFormer model with four different encoder variants. For pediatric cases (i.e., 0–7 years), the PedVision pipeline outperforms the best-performing models, achieving an 11.08 % improvement in Dice score over U-Net in the RSNA dataset and a 7.68 % improvement in the DHA dataset. When compared to DeepLabV3+, the improvements are even more substantial, with gains of 14.43 % in RSNA and 14.78 % in DHA. Additionally, PedVision shows notable advantages over the best SegFormer model, with improvements of 8.16 % in RSNA and 1.91 % in DHA. The project is open source at github.com/mohofar/PedVision.

Zinc (Zn) has emerged as a promising biodegradable metal for bone tissue engineering, yet fabricating porous scaffolds via laser-based additive manufacturing (AM) remains challenging due to Zn evaporation. This study presents the successful fabrication of porous Zn scaffolds via extrusion-based AM through systematic ink formulation and sintering optimization. Printability was optimized through rheological analysis of 50–56 vol % Zn-loaded inks, while sintering conditions were refined within a precise temperature window. SEM and micro-CT characterized sintering quality and quantified pore defects. Optimal scaffolds, printed with 53 vol % ink and sintered at 415 °C for 5 h, achieved 40 ± 3% absolute porosity with minimal evaporation, attributed to a hybrid solid-liquid phase sintering mechanism. The scaffolds exhibited trabecular bone-matching mechanical properties with compressive yield strength of 16.1 ± 1.3 MPa and elastic modulus of 1.4 ± 0.1 GPa. In vitro biodegradation in r-SBF showed a corrosion rate of 0.03 ± 0.01 mm/year after 28 days, with biodegradation products including ZnO, Ca₃(PO₄)₂, and Zn-phosphate/chloride hydrates. Electrochemical tests demonstrated increasing polarization resistance (21.1 ± 3.8 kΩ·cm²) and passivation behavior. Indirect cytocompatibility assays showed > 90% metabolic activity for MC3T3-E1 cells in ≤ 50% Zn extracts, while direct seeding confirmed cell adhesion. These results establish extrusion-based AM as a viable route for fabricating Zn scaffolds with tailored porosity, controlled biodegradation, bone-like properties, and acceptable cytocompatibility, advancing the development of Zn-based biodegradable implants. Statement of significance Although laser-based additive manufacturing of pure zinc and its alloys is becoming increasingly mature, its inherent drawbacks, such as evaporation-driven composition loss and melt-pool instabilities, remain non-negligible and underscore the need to develop and apply alternative AM strategies for Zn-based bone scaffolds. We presented an extrusion-based route to fabricate porous Zn bone scaffolds and establish an end-to-end workflow spanning ink formulation, debinding, sintering, and multi-scale characterization. By tailoring the binder system and defining a robust thermal window, we achieved high-fidelity architectures with densified struts. The resulting scaffolds displayed bone-mimicking mechanical behavior together with predictable in-vitro degradation and cytocompatibility. Our work positions extrusion-based 3D printing as a practical manufacturing platform for Zn-based biodegradable bone substitutes.

Osteochondral tissue engineering remains a significant challenge due to the complex biochemical and mechanical gradients between cartilage and subchondral bone. In this study, we present the development of a 3D-printed, multi-material magnetic hydrogel scaffold with tunable stiffness. To achieve this, we formulated a gelatin-alginate hydrogel matrix with various levels of embedded iron oxide magnetic particles (MPs) to create controlled hard-soft interfacial regions. The optimal composition (i.e . , 2.5% gelatin, 5% alginate, and 10% (w/v) MPs) demonstrated magnetorheological behavior, including increased effective Young’s modulus from 159 to 172 kPa and decreased viscosity from 175 to 145 kPa·s under a static magnetic field. Later, we evaluated scaffold printability through filament collapse, fusion, and porous scaffold tests, identifying a Gel:Alg ratio of 1:2 as optimal for structural fidelity. Mechanical and rheological characterizations confirmed that MPs significantly enhanced stiffness and responsiveness to magnetic fields. A checkered scaffold design enabled the fabrication of alternating hard and soft regions, and a bi-layered scaffold demonstrated improved interfacial adhesion. Micro-computed tomography provided quantitative evidence of magnetic field-induced particle redistribution within the hydrogel, confirming internal reorganization beyond bulk mechanical response. Importantly, in vitro live/dead assays confirmed that scaffold fabrication and magnetic functionality did not adversely affect cell viability. This platform offers a tunable, bioactive, and magneto-responsive scaffold architecture with potential for osteochondral repair or other applications requiring dynamic interface tissue engineering.

While conventionally manufactured metallic biomaterials can hardly meet all the requirements for bone implants including complex geometry, exact dimensions, adequate biodegradability, bone-matching mechanical properties, and biological function, two additional tools have recently appeared in the arsenal of biomaterials scientists which promise to deliver the desired combination of properties. First, the unique mechanical, electrical, and biological properties of graphene (Gr) and its derivatives (GDs), e.g., a Young's modulus up to 1 TPa, can be utilized to create metal matrix composites in which GDs of varied contents (typically not more than 2 wt%), sizes (lateral sizes from a few nanometers to several micrometers), surface areas (up to the theoretical value of 2630 m²/g), and layer numbers (typically up to 10) are embedded in the biodegradable metal matrix, thereby endowing the composite implants with extraordinary properties. Second, the distinct advantages of additive manufacturing (AM) make it possible for GD-containing composite materials to precisely mimic the complex shapes and structures of bones at multiple length scales. Here, a comprehensive review of the recent advances in the development of GD-containing biodegradable metal matrix composites (GBMMCs), ranging from composite fabrication, including composite powder preparation, and AM processes, to the evaluation of AM composites in terms of their mechanical and biological properties, is presented. Furthermore, the constraints in processing composite powders, the advantages and disadvantages of applicable AM techniques, and the mechanisms of mechanical reinforcement, biodegradation modulation, osteogenesis improvement, and cytotoxicity/antibacterial balance are critically analyzed. Thereafter, the foreseeable challenges faced in the development of the next-generation of bone implants based on GBMMCs are presented and some future directions of research are identified.

High-performance soft–hard interfaces are inherently difficult to fabricate due to the dissimilar mechanical properties of both materials, especially when connecting extremely soft biomaterials, such as hydrogels, to much harder biomaterials, such as rigid polymers. Nevertheless, there is significant clinical demand for synthetic soft–hard interfaces. Here, soft–hard interface geometries are proposed, designed with the aid of computational analyses and fabricated as 3D-printed hydrogel-to-polylactide (PLA) structures. Two primary interlocking geometries (i.e., anti-trapezoidal (AT) and double-hook (DH)) are used to study the envelope of 2.5D geometric interlocking designs, fabricated through hybrid 3D printing, combining pneumatic extrusion with fused deposition modeling. Finite-element analysis, uniaxial tensile tests, and digital image correlation (DIC) are used to characterize the geometries and identify parameters that significantly influence their mechanical performance. These findings reveal significant differences between geometric designs, where DH geometries performed significantly better than AT geometries, exhibiting a 190% increase in the maximum force, F_max, and a 340% increase in the fracture toughness, W. Compared to the control groups (i.e., flat, inset, and 90° interfaces), F_max and W values increased by 500%–990% and 350%–1200%, respectively. The findings of this study can serve as a guideline for the design and fabrication of efficient soft–hard interfaces with performances close to predicted values.

Magnetic particles (MPs), due to their unique physical and chemical properties, have emerged as promising tools in bone tissue engineering. Their incorporation into scaffolds or uptake by bone cells, combined with exposure to external magnetic fields, has been shown in various studies to enhance cell adhesion, proliferation, and osteogenic differentiation. In this review, the state-of-the-art is presented on the synthesis processes of magnetized cells (MCs) and magnetized scaffolds (MSs), as well as the biological and mechanical effects of scaffold-free MCs, cell-seeded MSs, and MC-seeded MSs under externally applied magnetic fields on bone tissue engineering. Furthermore, the specific applications of these systems is highlighted, such as non-contact mechanical stimulation, and discuss their application to advance bone tissue engineering strategies.

This Roadmap surveys the diversity of different approaches for characterising, modelling and designing metamaterials. It contains articles covering the wide range of physical settings in which metamaterials have been realised, from acoustics and electromagnetics to water waves and mechanical systems. In doing so, we highlight synergies between the many different physical domains and identify commonality between the main challenges. The articles also survey a variety of different strategies and philosophies, from analytic methods such as classical homogenisation to numerical optimisation and data-driven approaches. We highlight how the challenging and many-degree-of-freedom nature of metamaterial design problems call for techniques to be used in partnership, such that physical modelling and intuition can be combined with the computational might of modern optimisation and machine learning to facilitate future breakthroughs in the field.

Biocompatible and shape-morphing metallic structures have been proposed for musculoskeletal applications to provide structural support to bony tissues. However, fabricating these structures to conform to a wide range of curvatures, including both single and double curvatures, remains a significant challenge. In this study, we present and analyze structures featuring a regular tiling network connected by spherical joints, forming a chain mail-like mechanism capable of adapting to complex geometries with clay-like flexibility. Simulations using a multibody kinematics model show that parameters such as unit cell shape, dimension ratios, and substrate curvature affect the shape-matching abilities of the structure. Experimental validation using specimens additively manufactured through laser-based powder bed fusion (from Ti6Al4V) and full-field strain measurements performed through digital image correlation confirms the simulation results, demonstrating that reducing structural density (i.e., fewer bodies, struts, and joints per unit area) improves shape adaptability. However, the improved shape morphing capability often comes at the expense of mechanical strength under uni-axial tensile loads. These findings provide a framework for optimizing structures designed to achieve efficient surface conformance and adaptability in load-bearing applications.

High levels of physical activity or high BMI during puberty could negatively influence bone and cartilage development. Little is known about the effects of loading on patellar and femoral bone shape in a young population. Therefore, we aim to identify the association between 3D patella and femur shape and biomechanical loading in a young adolescent population. Participants were selected from an ongoing cohort study (Generation-R study). Participants that underwent knee-MRI at 13 years-old follow-up were included. Patellae and femora were segmented from these MRIs and using these 3D models, statistical shape modeling was performed. Generalized estimating equations were used to analyze the association between loading (BMI, physical activity and sports participation) and shape variation. Bonferroni correction was used to correct for multiple testing. 1912 participants underwent MRI of which 3638 patellae and 3355 femora were included in the statistical shape models. Nine patellar (modes 1–7, 10 and 11) and nine femoral (modes 1–3, 6–10 and 14) shape modes were associated with BMI. Sports participation at thirteen years old was associated with one patellar (mode 1) and two femoral (modes 1 and 6) shape modes. One shape mode (mode 12) was associated with sports participation at 9 and 13 years old. Sports participation and BMI were significantly associated with bone shape variations. BMI was associated with most shape variations found in our statistical shape models, emphasizing the significant impact of BMI on bone morphology during adolescence with implications for musculoskeletal health and injury prevention.

Incorporating shape-morphing capability into 3D microprinting enables the fabrication of 4D-printed microarchitectures as proof-of-concept actuators for potential use in soft robotics and microfluidic systems. The ability of these 3D microstructures to actuate rapidly and reversibly enables precise, non-invasive, and controllable deformation. In this study, we investigated the programmable shape-morphing behavior of 3D microarchitectures fabricated using two-photon polymerization (2PP) of a well-established temperature-responsive hydrogel, poly(N-isopropylacrylamide) (pNIPAM). We first systematically studied how 2PP 3D printing parameters (e.g., laser power, scanning speed) and the chemical composition of pNIPAM, including monomer and crosslinker, influence the shape morphing of bilayer microstructures within a temperature range of ~ 32 °C to 60 °C. The (thermo)mechanical properties of the hydrogels, including the Young’s modulus, thermal expansion coefficients, and angular deflection, were also measured at different laser doses and temperatures. Based on these experimental measurements, we calibrated a thermomechanical model capable of predicting the shape morphing of 4D-printed microarchitectures. These microarchitectures served as proof-of-concept actuators, demonstrating the potential of programmable microscale soft robotics and microfluidic systems. The findings provide design guidelines for engineering stimuli-responsive 3D microstructures, highlighting limitations and opportunities for future integration into functional soft robotic or microfluidic systems made of a single material.

The authors regret that the affiliation of Joseph Ahn was incorrectly listed in the original article. The correct affiliation is:

Independent Researcher, Hwaseong-si, South Korea

This error has now been corrected in the online version of the article.

The authors would like to apologise for any inconvenience caused.

The development of high-fidelity three-dimensional (3D) tissue models can minimize the need for animal models in clinical medicine and drug development. However, physical limitations regarding the distances within which diffusion processes are effective impose limitations on the size of such constructs. That is because larger-size constructs experience necrosis, especially in their centers, due to the cells residing deep inside such constructs not receiving enough oxygen and nutrients. This hampers the sustained in vitro growth of the tissues which is required for achieving functional microtissues. To address this challenge, we used three types of 3D printing technologies to create perfusable networks at different length scales and integrate them into such constructs. Toward this aim, networks incorporating porous conduits with increasingly complex configurations were designed and fabricated using fused deposition modeling, stereolithography, and two-photon polymerization while optimizing the printing conditions for each of these technologies. Furthermore, following network embedding in hydrogels, contrast agent-enhanced micro-computed tomography and confocal fluorescence microscopy were employed to characterize one of the essential network functionalities, namely the diffusion function. The investigations revealed the effects of various design parameters on the diffusion behavior of the porous conduits over 24 h. We found that the number of pores exerts the most significant influence on the diffusion behavior of the contrast agent, followed by variations in the pore size and hydrogel concentration. The analytical approach and the findings of this study establish a solid base for a new technological platform to fabricate perfusable multiscale 3D porous networks with complex designs while enabling the customization of diffusion characteristics to meet specific requirements for sustained in vitro tissue growth. Statement of significance: This study addresses an essential limitation of current 3D tissue engineering, namely, sustaining tissue viability in larger constructs through optimized nutrient and oxygen delivery. By utilizing advanced 3D printing techniques this research proposes the fabrication of perfusable, multiscale and customizable networks that enhance diffusion and enable cell access to essential nutrients throughout the construct. The findings highlighted the role of network characteristics on the diffusion of a model compound within a hydrogel matrix. This work represents a promising technological platform for creating advanced in vitro 3D tissue models that can reduce the use of animal models in research involving tissue regeneration, disease models and drug development.

Two-photon polymerization (2PP) is an additive manufacturing technology capable of producing polymeric 3D nano- to mesoscale structures with design flexibility and sub-micron resolution. This study investigates the influence of 2PP printing parameters on the morphology and mechanical properties of solid and porous microstructures fabricated from three commercial resins: IP-Q, IP-S, and IP-polydimethylsiloxane (IP-PDMS). To evaluate micromechanical behavior, micropillar compression tests are conducted using IP-Q, which has not been extensively characterized. Porous structures retained 80–85% of the stiffness of solids for IP-Q and IP-S, and 50% for IP-PDMS. Fourier transform infrared spectroscopy showed degrees of conversion of 38% for IP-Q and 61% for IP-S and IP-PDMS. The optimal printing parameters for IP-Q micropillars were a laser power of 50 mW, slicing distance (s) of 1.2 μm, and hatching distance (h) of 1 μm. These settings correspond to a peak laser intensity of 1.58 × 10⁻¹¹ W cm⁻², a focal spot diameter (dxy) of 3.17 μm, a Rayleigh length (zR) of 10.13 μm, and a voxel overlap (δ) of 0.6. These conditions yielded a Young's modulus of 3.7 GPa and yield strength of 75.21 MPa. Overall, the findings emphasize the challenges of parameter optimization when introducing porosity and comparing materials. The results provide a systematic framework for tailoring 2PP processing to guide biomedical microdevice design.

Existing deep learning (DL) networks are primarily trained on adult datasets and may not always generalize to pediatric populations, where growth plays a major role. Here, we investigated improving semantic segmentation outcomes of pediatric hand phalanges from radiographs without relying on fully pediatric training datasets, which are scarce. First, alternative DL networks (FCN-8, FCN-32, U-Net, Inception U-Net, and DeepLabv3+) were trained with manually segmented radiographs of near-skeletally-mature (NSM) subjects and their performances were evaluated using mean intersection-over-union (Mean IoU) and multiclass Dice scores. DeepLabv3+ and Inception U-Net performed the best for NSM segmentation, with Mean IoU scores of 0.899 ± 0.035 and 0.887 ± 0.062, respectively. These networks were then used to investigate zero pediatric data (scaling-based data augmentation) and minimal pediatric data (incremental pediatric data substitution) approaches to improve age-domain generalizability. The minimal pediatric data approach proved effective, with a 20 % pediatric data inclusion leading to an up to 21.1 % increase in Mean IoU for pediatric subjects compared to networks trained exclusively on NSM subjects. Furthermore, no adverse effects of this approach were found when tested on NSM subjects, and there were even improvements in performance for Inception U-Net. To conclude, we highlight that networks utilizing multi-scale filters perform best for the semantic segmentation of hand phalanges. We further demonstrate that a minimal inclusion of pediatric training data can markedly improve age-domain generalizability for semantic segmentation tasks. This removes the difficult task of gathering large training datasets of pediatric subjects, which is often impractical, if not impossible.

Recent developments in metal additive manufacturing (AM) processes have resulted in the fabrication of functional parts with reliable, reproducible, and predictable properties. Powder bed fusion (PBF) (e.g., selective laser melting and electron beam melting) and directed energy deposition (DED) techniques are the most common categories of AM technologies used for processing metals and their alloys. While the PBF processes can manufacture complex parts with structural gradations, the DED process offers advantages toward multi-material structures. Solid-state AM processes are also being developed for specific applications where sheet metals are readily available. This chapter addresses some critical aspects of various AM techniques, including processing parameters, material types, and geometrical design effects on the final productʼs quality, functionality, and properties. Finally, we discuss the challenges, limitations, and future outlooks for AM of metallic materials.

Tissue engineering approaches for cartilage tissue regeneration are expanding to include the complex features of the tissue, such as the biological and mechanical gradients. Many of these approaches are, however, based on the use of multiple biomaterials or concentrations, and crosslinking methods that make it difficult to integrate and control the properties of the resulting scaffolds. In this study, a 3D bioprinted scaffold with a stiffness gradient was fabricated by using a single biomaterial type and concentration combined with a directional ionic crosslinking method. The scaffolds revealed a gradient in stiffness from 39.8 ± 6.6 kPa at the top to 60.6 ± 10.9 kPa at the bottom of the scaffolds. Live/dead analysis of human chondrocytes embedded in the scaffolds showed no negative effects of the stiffness gradient on cell viability over 28 days. The induced stiffness gradient led to a gradient in cell density and sulfated glycosaminoglycan deposition in the bioprinted tissue constructs with enhanced values in the softer top region of the scaffolds as compared to the stiffer bottom part. This study showed a novel method to generate scaffolds with stiffness gradients from a single biomaterial and indicates that such scaffolds could be used to spatially regulate the behavior of chondrocytes and the associated deposition of the cartilage matrix.

Osteoimmunomodulation (OIM) is emerging as a key biofunctionality of orthopedic implants. Biomaterial surface geometries can modulate the interactions between immune cells and osteoprogenitors at the bone-implant interface, positively affecting osteogenic differentiation and implant osseointegration. This review highlights the recent advancements in geometry-induced OIM (G-OIM) across multiple length scales (nano to mesoscale, including multiscale topographies and 3D scaffolds), identifying relations between specific geometries and subsequent mechanisms of OIM, as provided by the coculture model used. Our review reveals surface geometries with OIM potential at each length scale. These effects can be mediated by both M1 and M2 macrophages, wherein the pathway depends on the shape and length scale of the geometrical cues provided (e.g., integrin-mediated mechanotransduction for nanoscale topographies and macrophage contact inhibition for micropatterns). Most studies assess G-OIM predominantly based on geometry-induced macrophage polarization and its paracrine effect on osteoprogenitors. However, few studies utilizing direct coculture reveal the key role of the direct interplay between macrophages, osteoprogenitors, and biomaterial for OIM. The novel field of G-OIM is advancing at a high pace. It could benefit from improved, clinically relevant coculture models involving human-derived cells and technological developments in biomaterial design and fabrication. Such advances could establish (G-)OIM as a transformative approach for regenerative immunoengineering of orthopedic implants. Statement of significance: Osteoimmunomodulation, the ability of biomaterials to modulate the interactions between immune cells and skeletal cells to enhance osteogenesis, is increasingly recognized as a crucial biofunctionality for orthopedic biomaterials. Various biomaterial surface geometries can be used to target osteoimmune pathways. Given the complexity of these interactions, suitable coculture models are essential for studying the underlying cellular mechanisms. This review reveals the state-of-the-art results on geometry-induced osteoimmunomodulation. Not only does this review discuss approaches that have been taken thus far in terms of biomaterial geometry design at various length scales, but it also highlights the role of the coculture model in osteoimmunomodulation and the importance of advances in these in vitro models to improve the translation of research to clinical practice.