Innovative animal models for surgical interventions and implant biocompatibility: a translational perspective

Abstract

Innovative animal models are instrumental in translating surgical and implant technologies, serving as bridges for surgical intervention and implant biocompatibility from preclinical research to clinical translation. This review discusses genetically modified, disease-specific, and biomimetic animal models used for orthopedic, cardiovascular, neural, and soft tissue implants. Quantitative comparisons have shown 45% impaired bone regeneration of CRISPR- engineered osteoporotic rodents, enabling exact evaluation of highly bioactive scaffolds. Humanized porcine models for vascular implants show similar 30% endothelialization improvement which reduce thrombosis risks, and enhance implant longevity. These advanced polymeric coatings were also able to reduce chronic rejection by 50% as measured in immune-humanized mouse models emphasizing the role of inflammatory response in qualitative analyses. Diabetic animal models with biosensor-integrated implants show 60% quicker wounds healing, underscoring the potential complement of smart implants and individualized preclinical experimentation. These advances have brought new challenges in genetic drift, long-term stability, and low regulatory standardization. The advancements of AI-assisted device selection, 3D bioprinting of those selected devices, and in vivo imaging of the devices, may prove to be the most promising for future studies. This review outlines a translational roadmap to intermediate cards with implant success and accelerates the clinical translation of next generation surgical technologies.

Introduction

Development and assessment of surgical procedures and implant biocompatibility are to a great extent based on preclinical animal models. The models give a fundamental platform for evaluation of the safety and effectiveness of new techniques and materials prior to translation into human clinical trials. But choosing a right animal model is of importance to get clinically valid results. This article will discuss novel animal models for surgical procedures and biocompatibility of implants and their potential for translation to better patient outcomes. In particular, we will discuss how meticulous model choice, taking into account limitations such as heterogeneous anatomy and differing healing rates, along with standardized procedures can bridge the translational gap between preclinical research and clinical success. We describe the difficulties in replicating the complexity of human physiology and pathology in animal models and consider how existing models can be improved and new models developed that better represent the human situation. One of the main features of the translational approach is the choice of animal models that closely represent the particular human condition under investigation. This necessitates close attention to the anatomy, physiology, and immune response of the animal in comparison with the human condition. It must be recognized that no animal model can exactly duplicate the human situation. One of the major difficulties comes from disparate anatomy and rates of healing. Large animal models tend to be the choice for clinical trials, whereas small animal models are used in pathophysiological pathway analysis. Thus, more than one model might be required to cover various aspects of implant performance and biocompatibility. Finally, standardized protocols and ethical principles are essential for making preclinical research reliable and reproducible. Following these guidelines, we can ensure the maximal translational value of animal studies and expedite the creation of new surgical techniques and biocompatible implants^[1].

Significance of animal models in surgical implant research

Animal models are an important part of surgical implant research because they serve as a critical link between in vitro testing and human clinical trials. They offer a sophisticated living system to evaluate the biocompatibility, effectiveness, and safety of new implant materials and designs under controlled conditions. Using preclinical trials, scientists can research tissue reactions, measure implant integration, and analyze long-term outcomes prior to advancing to human applications. This holds special significance in neurotechnology, where the complex interface between implants and nervous tissue needs to be examined with caution^[1,2].

More broadly, animal models allow for study of the interaction between host tissue and implant material in a live organism. This includes evaluation of inflammatory responses, development of fibrous capsules, and overall tissue incorporation of the implant. Rabbit models, for example, are usually chosen due to similarities with humans in bone structure and healing processes^[3]. Animal models enable the testing of how well an implant is integrated into the surrounding tissue, crucial for long-term support and function. Biomechanical testing, such as push-out and pull-out testing, is commonly employed to quantify the extent of osseointegration. Researchers are able to monitor the performance and tissue reaction of the implant in the long term, which provides significant information about long-term stability, complications, and issues of wear^[1]. Animal models help to determine the effectiveness of the implant in restoring its intended function, such as restoring mobility or sensory function. They also allow researchers to identify potential safety concerns and side effects before human trials are carried out. Certain animal models can mimic specific human diseases or conditions, providing a setting to test the performance of the implant under a more clinically applicable scenario. This is particularly useful in the case of dental implant research, where animal models are able to mimic clinical scenarios^[4].

Evolution of preclinical models for implant biocompatibility

The area of preclinical models used for implant biocompatibility testing has dramatically shifted. While macro-level function, including the occlusion of the implant, in the past has been the advantage with the large animal model, there is present interest now for the study of intricate pathophysiologic events and molecular interaction at the bone/implant interface in small animal models. This shift is driven by a variety of reasons (i) Cost-effectiveness: Small animals like rabbits, rats, and mice are less expensive to maintain and work on compared to big animals like sheep, goats, or non-human primates^[5]. (ii) Ethical concerns: The application of smaller animals is consistent with the “reduce, refine, replace” guideline for animal experimentation, reducing the number of animals involved and maximizing experimental procedures to reduce pain and distress. (iii) Better understanding of bone biology: Techniques in advanced imaging and molecular biology enable scientists to retrieve more sophisticated information from reduced samples, thereby making smaller animal models more and more useful. (iv) Address individual mechanisms: There is often a preference to use smaller animal models to understand certain cellular and molecular mechanisms for osseointegration, inflammation, and remodeling around implants^[6].

HIGHLIGHTS

• Genetically modified, disease-specific, and biomimetic animal models are used for various implants

• Quantitative comparisons show 45% impaired bone regeneration in CRISPR-engineered osteoporotic rodents.

• Humanized porcine models show 30% endothelialization improvement in vascular implants.

• Diabetic animal models show 60% quicker wound healing with biosensor-integrated implants.

• Challenges include genetic drift, long-term stability, and low regulatory standardization.

• Future studies may focus on ai-assisted device selection, 3D bioprinting, and in vivo imaging.

Concurrently, in vitro systems have reached incredibly high sophistication, offering the investigator potent methods to answer specifically defined research questions. These now involve 3D cell culture systems and refined imaging modalities. Nevertheless, despite these breakthroughs, it remains impossible with in vitro systems to exactly mirror the intricate relationship between cells, tissues, and systemic influences as exists in the intact organism. Preclinical in vitro testing is important in assessing different properties of implantable biomaterials. These range from traditional methods such as cytotoxicity tests to newer models that more closely simulate the in vivo environment. In vitro testing is also important in determining the degradation characteristics of bioabsorbable devices^[7,8].

The choice of an optimal preclinical model depends on a number of factors. The model must fit the particular questions of research, whether it is assessing bone integration, inflammation, or long-term function. The size and type of the implant under investigation also determine the choice of the animal model, in that larger implants may require larger species. Physical properties of the implant determine the model choice. Smaller implants tend to be piloted using smaller animals, whereas bigger implants might need bigger animals. More and more, there is a desire to select the optimal model and most ethically acceptable model. Physiological human likeness, such as bone formation and healing mechanisms, must be considered. Bone density and rate of turnover processes can have a significant impact on the translational relevance of the data^[9].

The ultimate long-term objective is to develop an exhaustive and reliable preclinical evaluation strategy that incorporates proper animal models and complementary in vitro approaches to enhance the predictability of successful clinical translation. This calls for precautionary assessment of factors including the functional features of the biomaterial, bio response, and the intended orthopedic function. Such an integrative process is intended to accelerate the development of secure, functional, and biocompatible implants with improved patient outcome^[10]. Table 1 describes genetically engineered animal models (GEMs) for surgical interventions.

Table 1.

Genetically engineered animal models (GEMs) for surgical interventions^[11–13]

GEM type	Genetic modification	Purpose in surgical research	Common animal models	Example applications
Knockout (KO) Models	Specific gene deletion	Studying gene function, disease modeling	Mice, rats, pigs	Wound healing, organ regeneration
Knock-in (KI) Models	Insertion of specific genes	Testing novel therapeutic targets	Mice, pigs	Stem cell therapies, biomaterial integration
Transgenic Models	Introduction of foreign genes	Studying overexpression effects	Mice, rabbits, zebrafish	Cancer surgery, tissue repair
Conditional KO/KI	Gene modification controlled by external factors (e.g., Cre-LoxP)	Studying stage-specific or tissue-specific effects	Mice, pigs	Organ transplantation, tissue engineering
Humanized Models	Replacement of animal genes with human genes	Testing human-specific surgical treatments	Mice, pigs, primates	Drug testing, xenotransplantation
Reporter Models	Fluorescent or luminescent markers added	Tracking cells, tissue healing, or biomaterial integration	Mice, zebrafish	Live imaging of surgical outcomes
CRISPR/Cas9-Edited Models	Precise gene editing	Rapid development of disease models for surgical research	Mice, pigs, primates	Gene therapy, regenerative medicine
Epigenetically Modified Models	DNA methylation or histone modification changes	Studying epigenetic regulation in surgical healing	Mice, rats	Scar formation, chronic wound healing
Inducible GEMs	Gene expression controlled by external agents (e.g., doxycycline, tamoxifen)	Temporal control of gene function in surgery-related studies	Mice, pigs	Post-surgical tissue regeneration
Optogenetic Models	Light-sensitive genes introduced	Controlling cellular responses during surgery	Mice, zebrafish	Nerve regeneration, brain surgery
Organoid-Derived GEMs	Genetically modified stem cells used to create organ-like tissues	Personalized surgical models	Mice, pigs	Organ transplantation, bioengineered tissues

Types of animal models for surgical interventions

Animal models play a key role in developing and refining surgical techniques. They provide a setting for experimentation with novel techniques, devices, and therapeutic strategies before applying them to humans^[14]. Selection of the appropriate animal model depends upon a variety of factors, from the type of surgical procedure, the question under investigation, to ethical considerations.

Genetically engineered models

Genetically Engineered Models are a very efficient instrument for surgical research. Scientists construct GEMs by altering an animal’s genome, typically by applying techniques like CRISPR-Cas9. This allows researchers to create models of human genetic disease or disease process in an individual that can be researched to comprehend disease mechanisms. With the alteration of individual genes, researchers come to comprehend the molecular mechanisms involved in disease. This data plays a significant role in developing individualized surgical interventions^[15]. GEMs may be engineered to express specific phenotypes of disease in order that scientists can watch how the disease grows and influences the living organism. GEMs provide a controlled setting to experiment with new surgical techniques, devices, and gene therapies. For instance, researchers can try out the safety and efficacy of a new surgical procedure in a GEM before testing it in humans. GEMs also hold the promise to construct humanized models where human genes, cells, or tissues are added, so that the model is more applicable to human biology and potentially the translation of research findings to the clinic^[16]. Surgical treatment in the future could involve treatments tailored to individual genetic profiles. Implementation of GEMs could facilitate such customized tactics being modeled and tested, paving the way for improved precision and improved surgical care. This follows with greater priority on precision medicine. Customized models of pre-surgery preparation and specially manufactured devices could come next^[17]. GEMs are important tools in evaluating and testing the biocompatibility of devices and implants. Researchers can explore the long-term effects of such implants in vivo, leading to improved designs and materials. GEMs, particularly mice and rats, are now essential to product development and discovery, particularly in medical therapeutics. They allow novel drug targets to be identified, disease mechanisms to be understood, and product efficacy and safety to be tested^[12]. However, it is important to note the confines of such models. GEMs are good but not ideal models of human conditions. Safety of research demands adherence to meticulous interpretation of results and familiarity with the model’s limitations. In addition, use of GEMs also carries inherent ethical issues that have to be addressed seriously, such as concern for animal welfare and wise use of genetic modification technology. Preclinical trials may be performed in special institutions such as EXPLORA under strict GLP conditions^[18].

Disease-specific and humanized animal models

Disease-specific models are built to replicate particular human diseases, giving a system through which disease mechanisms can be studied and possible drugs tried. Humanized models build on this by adding human genes, cells, or tissues to the system, creating a system more similar to human biology. This is typically accomplished by xenotransplantation and genetic engineering methods. Such models allow surgeons to practice and master new surgical skills in a secure setting, with minimal risks when applied to human patients^[19]. Studies documents the use of non-living models of animals in cranial neurosurgical education. Researchers apply disease-specific models to test the effectiveness of surgical tools and implants. For example, the rat model for bone regeneration testing. Pigs, sheep, and dogs are popular large animal models for pre-clinical trials due to their size, which is sufficient to fit human-sized surgical instruments. Humanized and disease-specific models of animals are employed to evaluate new drugs and treatment modalities in an environment that closely mimics human disease^[20,21]. This provides a more accurate prediction of potential efficacy and safety before human trials. Humanized animal models are essential to unravel the complex interaction between human cells/tissues and disease processes. By incorporating human components into animal models, researchers can actually see how human tissues and cells respond to surgical procedures, providing a better reflection of potential outcomes in human patients. Humanized models facilitate better translation of preclinical information to human trials by reducing discrepancies potentially arising due to species-specific differences^[22]. These models allow research into multifactorial human diseases like cancer, cardiovascular disease, or neurological disorders, which are often difficult or impossible to emulate in traditional animal models. Humanized models have the potential to enhance the predictive validity of preclinical research by a more accurate representation of human responses to surgical interventions and new therapies^[23].

Emerging biomimetic and 3d bio-printed models

Biomimetic and 3D Bioprinted Models represent a latest development in the field of surgery, with new innovations in caring for patients coming in the future and accelerating new surgical devices and methods. Biomimetic models try to replicate the complex shapes and functionality of human organs and tissues in a laboratory setting using a controlled biological system for examining biological processes^[7]. 3D bioprinting is a step beyond in allowing scientists to create detailed tissue structures and organoids with direct control over where the cells and what material go. 3D bioprinting is possible to be used to build patient-specific models derived from clinical imaging data such as CT or MRI scans. These tailored models have the potential to revolutionize pre-surgical planning by allowing surgeons to visualize the unique patient’s anatomy and practice complex procedures in advance, prior to stepping into the operating room. Furthermore, they allow for personalized device design, where the prosthetics and implants are designed specifically for each patient^[24,25]. Biomimetic and 3D bioprinted models provide a robust alternative to animal testing, with the potential to reduce the number of animals employed in surgical research and ease ethical concerns. Here, the models can be tested in initial trials for new surgical techniques, equipment, and implants, refining them in a controlled environment before advancing to preclinical studies^[26,27]. The degree of control that biomimetic models offer enables researchers to study the intricacies of tissue repair and regeneration in specific detail. Researchers can control the cellular and material make-up of the models and the conditions in the environment to a certain degree, whereby they can isolate and study distinctive biological processes. Such control is priceless in disease process studies and the design of targeted therapeutics. The development of biocompatible implants is a significant area of emphasis in surgical research^[12,28]. Animal models have traditionally been critical for assessing the biocompatibility and long-term efficacy of implants, including underappreciated facets of biocompatibility that can impact surgical outcomes. Though such models are still valuable, in vitro biomimetic models provide a complementary platform for preliminary biocompatibility evaluation, with a cost-saving and potentially faster way of screening materials^[29]. The establishment of these in vitro models has improved (i) Bone Repair Research: Animal models are vital in bone repair, allowing researchers to comprehend healing processes and formulate successful therapies. 3D printing is currently applied to develop bone-like constructs, providing a new platform for exploring bone regeneration and evaluating novel biomaterials in a controlled system. (ii) Veterinary Diagnostic Imaging: AI has transformed veterinary diagnostic imaging to improve the accuracy and speed of image analysis. This technology will allow researchers to evaluate the effectiveness of surgical procedures in animal models and gain information that is pivotal for translational research^[30,31]. As illustrated in Fig. 1, wearable health monitoring systems involve a broad spectrum of considerations including body fluids, diagnostic indicators, clinical trials, and manufacturing strategies.

Figure 1.

Multifaceted considerations in wearable health monitoring systems.

Implant biocompatibility assessment in animal models

Biocompatibility testing of implantable devices is critical in determining their safety and efficiency before their application in humans clinically. Animal models play a very vital role herein, providing valuable information on the interaction between the biological system and the implant. Selection of a proper animal model hinges on factors such as the specific application of the device, the problem under investigation, and ethical considerations.

Osseointegration and bone regeneration studies

Osseointegration, the stable anchorage of an implant by direct bone-to-implant contact, is a long-term success determinant for orthopedic implants. It is a complex biological phenomenon decided by both the implant and host bone response properties. Animal model choice is a central component for osseointegration research. Features of bone quality, healing character, and size must be thoroughly studied in a manner to ensure utility for human clinical scenarios^[32]. The rabbit, sheep, dog, and pig models are employed most frequently and all share weakness and advantage. Sheep bone size and form, for instance, make them suitable for large implantation, and rabbits’ increased healing rate might be capable of pushing research forward. Other than species selection the material’s biocompatibility itself matters^[33]. Titanium and titanium alloys are preferred for their biocompatibility and Osseo integrative nature. Surface properties of the implant, including roughness and porosity, also affect bone-implant interaction. Methods such as sandblasting and acid etching can alter these properties to enhance osseointegration. Additively manufactured implants, by virtue of 3D printing, provide precise control over these parameters and have the potential to promote enhanced osseointegration^[34]. Reliable and careful surgical practice is necessary to reduce trauma and maximize the osseointegration conditions. The preparation of the implant site, method of inserting the implant, and primary stability during surgery all influence later bone healing. The mechanical environment of the implant critically impacts osseointegration. Controlled loading protocols of animal models can mimic physiological stresses and strains, facilitating bone adaptation and implant stability^[35]. On the other hand, excessive or insufficient loading will hinder osseointegration and result in implant loosening. Biomechanical tests like push-out and pull-out tests yield quantitative indices of the bone-implant interface strength. Histological examination enables microscopic examination of the bone formation and implant integration. Imaging methods, such as micro-computed tomography (micro-CT), provide non-destructive methods to examine bone structure and mineral density around the implant^[36]. The selection of the proper animal model and study parameters will largely be based on the nature of the research questions. Preclinical testing involving bioabsorbable implants would involve considerations other than those testing permanent metal implants. Degradation rate and the tissue reaction to degradation products become considerations. More complicated designs, such as osseointegrated transfemoral prostheses, will usually require larger animal models and specialized surgical methods.

Cardiovascular implant integration and endothelialization

Cardiovascular devices, like stents, heart valves, and vascular grafts, have a distinctive set of biocompatibility problems. Integration depends on reducing undesirable interaction with blood and adjacent tissues. Hemocompatibility, which describes the capacity of a material to function as desired in contact with blood, is the most critical issue^[37]. Thrombosis, blood clotting, is a major concern, since it can result in fatal complications. Encouraging immediate and total endothelialization, the establishment of a healthy layer of endothelial cells on the surface of the implant, is most important for long-term success^[38]. This layer of endothelium simulates the body’s own lining to blood vessels and is vital in avoiding thrombosis and governing vascular tone. Animal models are required to evaluate the in vivo performance of cardiovascular devices. Model choice is dependent on device and question being studied. Pigs and sheep are often chosen due to similarity with human cardiovascular anatomy and physiology. Dogs and rats are used, particularly for smaller graft diameters. The extent and quality of endothelialization can be assessed by various techniques, including microscopy and immunohistochemistry^[39]. Endothelialization depends on the characteristics of the implant material surface, the bioactive coatings available, as well as the local hemodynamic conditions. Techniques to enhance in situ endothelialization, such as the recruitment and differentiation of endothelial progenitor cells, are being actively researched. The proclotting nature of an implant is quantified by observing platelet adhesion and activation, coagulation cascade markers, and thrombus formation frequency. Endothelium-mimetic surface alteration attempts to improve the antithrombogenicity of implants. The inflammatory response to the implant can influence long-term patency and endothelialization^[40]. Cell infiltration by inflammatory cells, release of cytokines, and expression of inflammatory markers can be examined in animal models. The excess growth of smooth muscle cells within the vessel wall, or neointimal hyperplasia, can lead to stenosis (narrowing) of the vessel lumen. Animal models enable investigation of neointimal hyperplasia determinants and testing of ways to reduce it^[41]. Vessel wall long-term adaptation to the implant is an important integration area. Animal models enable the monitoring of changes in vessel diameter, wall thickness, and extracellular matrix composition over time. For those implants, such as stents and vascular grafts, particular focus is on the patency (open) of the device and stenosis. These parameters are followed using in vivo imaging techniques, such as angiography and intravascular ultrasound. For more sophisticated devices, like artificial heart valves, there are specialized animal models and surgical techniques required^[42].

Neural implants and bioelectronic interfaces

Neural implants and bioelectronic interfaces are a fast-developing area with the promise to transform the therapy of neurological disorders and augment human capabilities. The devices directly interface with the nervous system, either to capture neural activity or to provide electrical or chemical stimulation^[43]. Yet, the intricate and delicate nature of the nervous system introduces special biocompatibility issues. Animal models are critical for assessing these devices prior to human trials. Rodents, especially rats and mice, are often used because they are relatively small in size, handle easily, and have available genetic tools^[44]. Non-human primates are employed for those studies that call for more complex behavioral or cognitive assessments, even though ethical grounds demand proper justification. Introduction of any foreign material into the nervous system causes a tissue response. This tissue response may consist of inflammation, glial scarring (a barrier-like structure developing around the implant made up of glial cells), and damage to the neurons^[45]. The magnitude and character of this response can have a significant effect on the long-term functioning of the implant. Reducing the foreign body response is one of the major objectives of bioelectronic implant design. Long-term neural implants need stable electrode-tissue interfaces for consistent signal recording or stimulation. Electrode material biocompatibility, electrode geometry, and mechanical tissue properties may affect electrode stability^[46]. The stability of the electrodes in maintaining their function over time in terms of signal-to-noise ratio and stimulation efficacy is another essential consideration. The creation of soft, flexible bioelectronics is also expected to facilitate better compatibility with the brain’s soft tissue and minimizing the foreign body response. Most contemporary neural implants now include wireless communication and power transfer features^[47]. This reduces the risk of wires that can limit movement and lead to infection. Wireless systems need to be designed to have minimal power consumption and heat generation in the body. Bidirectional communication and ultrasonic powering are becoming promising techniques for small-scale implantable stimulators. The final aim of most neural implants is to recover lost function or to improve capabilities already present^[48]. The use of animal models permits measurement of functional outcome in the domains of motor control, sensory perception, and cognition. Behavioral examinations are designed for the particular use of the implant, i.e., determining gait in spinal cord injury models. Electrophysiological recordings are important for tracking brain activity and the interaction of the device with the nervous system^[49]. Although developments in surgical methods and implant technology have greatly enhanced the biocompatibility of neural implants, there are still many challenges to be addressed. The creation of the next-generation devices will involve interdisciplinary collaborations between materials science, neuroscience, and engineering. Miniaturization, better biocompatibility, and integration of multimodal functions are key research avenues^[50]. Table 2 explains the key parameters for implant biocompatibility assessment in animal models.

Table 2.

Key parameters for implant biocompatibility assessment in animal models^[7,51]

Parameter	Description	Evaluation method	Expected outcome
Animal model	Type of animal used for testing	Rodents, rabbits, pigs, primates	Appropriate physiological response
Implant material	Composition of implant material	Metal, polymer, ceramic, composite	Biocompatible and non-toxic
Surgical procedure	Implantation method and site	Subcutaneous, intramuscular, orthopedic	Proper fixation without complications
Inflammatory response	Body’s reaction to the implant	Histological analysis, cytokine levels	Minimal inflammation
Tissue integration	Degree of implant integration with tissue	Histology, microscopy, imaging	Good integration with minimal fibrosis
Foreign body reaction	Immune response to implant	Presence of macrophages, giant cells	Absence or minimal reaction
Toxicity	Systemic effects of the implant	Blood tests, organ histology	No systemic toxicity
Degradation & wear	Breakdown of implant over time	Microscopy, weight loss, mechanical testing	Controlled degradation (if applicable)
Mechanical stability	Structural integrity of implant in vivo	Mechanical testing, imaging	Maintains function without failure
Osseointegration (if applicable)	Bone integration with implant	Micro-CT, histology	Strong bone-implant bonding
Hemocompatibility (if applicable)	Interaction with blood	Coagulation tests, platelet adhesion	No thrombotic activity
Long-term effects	Chronic response to implant	Follow-up over months/years	No long-term adverse effects

Quantitative and qualitative evaluation of implant performance

Evaluation of implant performance has to be interdisciplinary, incorporating quantitative biomechanical tests as well as qualitative evaluation of tissue response and long-term stability. Both play a crucial role in the assessment of safety and efficacy of implants as well as the guidance of design and surgical technique development.

Biomechanical testing and load-bearing analysis

Biomechanical testing plays a significant role in determining the mechanical performance and integrity of implants when subjected to controlled physiological conditions. The tests provide useful information about an implant’s ability to withstand various forces and stresses, and this helps researchers investigate its capacity to bear load and withstand failure^[52]. Static Testing method applies a constant load to the implant in order to determine its ultimate strength (maximum stress at failure), yield strength (stress at permanent deformation), and stiffness (resistance to deformation). This information is vital in ensuring that the implant will be able to endure physiological loads without fracturing or undergoing permanent deformation. Static testing is generally utilized to examine different surface coatings of implants^[53]. Dynamic testing technique implants are seldom exposed to continuous loads in vivo. Dynamic testing overcomes this by using cyclic loading, mimicking the repetitive stresses seen with activities such as walking or chewing. This form of testing measures fatigue life (cycles to failure) and resistance to crack propagation, and is very helpful in determining the long-term integrity of the implant. The nature of dynamic test, and particular parameters, must be decided on a case-by-case basis, depending on the device, anatomical site, and proposed use^[54]. Torsional testing test involves applying a twist to the implant, particularly significant for dental implants and other rotationally stressed implants. Torsional testing examines the twisting strength of the implant and ensures it can resist stability and function loss under rotational forces. Wear testing is most significant for articulating implants, such as joint replacements. It mimics the wear and fatigue that happens at the articulating surfaces, enabling the evaluation of the implant’s durability and forecast its lifespan. Certain testing technique, including the duration, use of lubricant, and load type must be tailored to the intended implant^[55]. Finite element analysis (FEA) is a sophisticated computer program that simulates the distribution of stress and strain within implants and bone tissue. Through taking various loading conditions into account, FEA is able to make estimates of implant performance and determine stress concentrations leading to failure. Such data is highly valuable to optimizing implant design and improving surgical technique^[56]. Resonance frequency analysis (RFA) measurements are significant in the determination of the long-term stability of the implant. This technique measures the vibrational frequency of the implant, which is linearly correlated with bone-implant interface stiffness. RFA is particularly useful in monitoring osseointegration and ascertaining long-term implant stability. The position and size of the implant are known to influence stability, so a detailed evaluation is needed for long-term success^[57].

Histopathological and inflammatory response assessments

Histopathological analysis of peri-implant tissue is of great importance to comprehend tissue response and integration of the implant. Cellular and tissue-level alterations observed by researchers are utilized for testing biocompatibility and complications. The nature and degree of inflammatory reaction are disclosed by the occurrence and the type of inflammatory cells (neutrophils, macrophages, lymphocytes) that show up in the vicinity of the implant. A balanced inflammatory response is needed for healing to be successful, while an excess or chronic one can hinder integration and lead to implant failure^[10]. For example, macrophages have a multifaceted role in dental implant failure. A fibrous capsule is a connective tissue layer that tends to form around implanted devices. The cellularity and thickness of the capsule may indicate the extent of foreign body reaction. Thick, avascular capsules are often seen with an enhanced foreign body reaction and might be predictive of complications. Fibrous encapsulation is exemplified in the case of silicone breast implants but, according to further research, the existence of the capsule itself is not equivalent to any undesirable health results^[58]. Successful osseointegration, direct structural and functional association between living bone and the surface of the implant, is mandatory for long-term stability in the case of orthopedic implants. Histological evaluation assists with quantifying formation of new bone, assessing the quality of new bone, and how much bone-contact there is at the implantation site. Successful osseointegration is desirable, but remodulation of tissue around the implant must also be taken into consideration when evaluating for stability. The development of new blood vessels (vascularization) of the peri-implant tissue is essential for tissue regeneration, delivery of nutrients, and long-term implant stability^[1]. Histological evaluation may uncover the distribution and density of new blood vessels, giving information about healing and the general condition of the surrounding tissue. Besides routine histological staining, immunohistochemistry allows researchers to identify single cell types and molecules involved in the tissue response. By means of IHC, researchers achieve a better understanding of the complex biological interactions at the implant-tissue interface, which improves our knowledge of biocompatibility and supports biomaterials design^[59]. Specifically, IHC staining is most commonly used to evaluate implant biocompatibility. Histopathological and inflammatory response testing play an essential role in the evaluation of implant biocompatibility and predicting long-term performance. They help researchers gain insight into complexities between the implants and tissue interaction at a proximity level, thereby resulting in safer and more efficient implantable devices^[60].

Imaging techniques for real-time implant tracking

Imaging techniques are important for long-term follow-up of implant position, stability, and integration with the surrounding tissue. They provide valuable information about the interaction between the implant and the bone and can identify potential complications. Radiography universally available and cost-effective method creates two-dimensional representation of surrounding bone and implant. Radiography can effectively measure initial implant placement, track bone remodeling, and pick up gross change in bone density. It’s also commonly used after surgeries to evaluate the overall condition of the implant bed and monitor bone healing and osseointegration^[48]. However, its 2D character limits it from being capable of providing detailed information on complex three-dimensional structures. For example, cross-sectional imaging is to be preferred over regular radiography when assessing the width of bone in dental implants. Computed tomography (CT) scans create three-dimensional images of the implant and surrounding tissues, giving more accurate information about bone density and the integration of the implant^[61]. CT is particularly useful in assessing complex anatomical regions and imaging the bone-implant interface. It is able to identify subtle changes in bone architecture that are not apparent on radiographs. CT scans, however, involve higher radiation exposure than radiography, which needs to be considered. Magnetic Resonance Imaging is better in soft tissue contrast and is particularly valuable in evaluating the tissue response to implants. MRI can visualize inflammation, edema, nerve regeneration, and other soft tissue disease that is poorly illustrated with CT or radiography. It is often used to quantify the location of cochlear implant receivers, with reference markers placed on the patient’s head^[62,63]. MRI is, nonetheless, vulnerable to metal artifact from the implant itself, which in certain applications can limit image quality near the implant. It is worth mentioning that the development of artificial intelligence algorithms for automated image quality assessment may further push MRI in this regard. Nuclear imaging techniques, such as positron emission tomography and single-photon emission computed tomography, use radiotracers for visualizing the blood flow and metabolic function of the peri-implant tissue. They provide valuable information about healing, inflammation, and tissue viability. Nuclear medicine is also utilized to monitor distribution and activity of implantable drug delivery devices^[43,64]. Nuclear imaging, conversely, has lower spatial resolution compared to other modalities and involves radiotracer injection. The selection of imaging modality is based on the particular clinical question, implant type, and characteristics of surrounding tissues. Imaging modalities tend to be combined in many instances to achieve an overall understanding of implant performance and tissue response. In addition, new developments in multiscale sensing technologies, including capacitive technologies, hold potential for enabling more accurate and continuous assessment of bone-implant loosening, with a possible supplemental role compared to conventional imaging techniques^[65]. As shown in Fig. 2, biocompatibility assessment is influenced by a complex interplay of parameters including implant-related factors, temperature, homeostasis, pharmaceutical interactions, health status, and experimental models.

Figure 2.

Key Factors in biocompatibility assessment of medical implants.

Technological innovations in implant testing

Implant testing must be done for safety and efficacy before clinical use. Traditionally, this has been done with in vivo animal models, but there is a fast-growing trend towards using in vitro and in silico methods. This is driven by ethical and cost-related reasons, and the potential for more precise, human-specific information^[66]. Emerging technology is revolutionizing implant testing and has tantalizing potential for personalized medicine and improved patient outcomes.

Ai-driven predictive models for implant success

Artificial intelligence is rapidly changing healthcare, and implant dentistry is not left behind. AI algorithms have a good capability of analyzing vast and complex sets of data, which in this instance can prove useful in successful implantation prediction. Such sets of data can include preclinical studies, clinical trials, and real patient information. By pattern identification and correlation within this data, AI can forecast successful osseointegration, long-term stability, and potential complications such as peri-implantitis^[67,68]. These predictive models control for a high number of parameters, including patient characteristics (age, sex, medical history, smoking status, and hereditary aspects); implant characteristics (material, morphology, surface topography, and size); surgical planning (implant placement, bone quality and quantity, and surgical technique); and biomechanical forces (stress pattern, micromotion, and loading conditions). Through these variables’ incorporation, AI has the ability to foretell the risk of implant failure and guide clinicians in deciding more effectively about how to plan a treatment. With AI, designing implants and surgeries can be made tailored for personal patient parameters, bringing forth patient-specific implantology^[69]. For example, AI can be utilized to make predictions on the optimal size, location, and angulation of an implant for a patient’s bone structure and density. Such personalization can enhance implant success significantly and reduce the likelihood of complications. While still in the development phase, AI-based predictive models have great promise for the future of implant dentistry. As such models become more advanced and integrated into clinical practice, they are sure to be contributing more towards ensuring successful and predictable outcomes of implants^[70].

Biosensor-integrated smart implants

Inclusion of the biosensors into the implants is rationale to create “smart implants” which have the capability of detecting the implant-biological interface in real time. The sensors represent a monumental innovation in implant technology, evolving from passive implants to active, adaptive implants. The real-time feedback loop further results in new possibilities for individualized treatment and improved patient outcomes. Intelligent implants utilize a collection of biosensors to measure crucial information about the functioning of the implant and the biological environment in which it exists. Smart implants utilize an array of biosensors to gather critical real-time data about the implant’s operation and surrounding biological environment^[71]. Strain can be measured by these sensors, which makes it possible to assess the mechanical stability of the implant and its integration with the surrounding tissue. Measurement of micromovements or undue strain provides warning of loosening or failure. Force sensors monitor forces applied to the implant as well as to tissues, particularly on load-bearing implants^[72]. Temperature readings at the implant site may indicate infection or inflammation. pH changes may also reflect tissue infection or injury. Finally, biosensors can measure biomarkers, which are biological molecules which can indicate specific conditions or diseases such as inflammation, infection, or bone repair. The data collected by these biosensors are helpful in knowing the healing process and are used to identify the onset of complications: Smart implants are able to continuously monitor the implant-tissue interface, and in doing so, are able to detect early warning signs of loosening, infection, or other responses prior to causing any damage, allowing early intervention. Data obtained by sensors can be used to modulate stimulation parameters or drug delivery based on the requirement of the patient to Facilitate individualized treatment regimens^[73,74]. For instance, a smart implant can release pain medication, as and when required, according to real-time pain signals recorded by biosensors. Data from clever implants can direct the making and designing of more advanced future implants, that may be even better, work for longer periods of time, and be even biocompatible. Although enormously potential, intelligent implants are challenged by the delivery of a robust and reliable source of power, effectively communicating the sensed data from sensors to offboard devices, the long-term compatibility with tissues, and handling the added cost of their inherent complication^[13].

3D bioprinting for customizable preclinical models

3D bioprinting is revolutionizing preclinical testing of implants by allowing the creation of advanced, patient-specific tissue models. The printed models exactly mimic the intricate structures and heterogeneous composition of human tissues like bone, cartilage, and soft tissues and provide a much more realistic test environment than the traditional approaches^[75]. This allows performance of implants, biocompatibility, and host tissue integration to be assessed with greater accuracy. One of the major advantages of 3D bioprinting is that it can create customized implants tailored to suit a particular patient’s needs. This customization can significantly improve the fit, functionality, and longevity of implants. Using the assistance of medical imaging information, such as CT or MRI scans, 3D bioprinters can use the information to print implants with precise geometries and material properties that are comparable to a patient’s specific anatomy^[76]. This level of precision can lead to better integration into surrounding tissue, fewer complications risks, and better overall outcomes. 3D bioprinting also supports in vitro disease models. These models may replicate the patient’s specific pathophysiology of disease, such as bone cancer or osteoporosis, and researchers may subsequently evaluate the efficacy of novel implants and treatments within a controlled environment^[77]. This approach has the potential to reduce the reliance on animal models significantly, accelerating the development of new treatments and improving the translation of research results to the clinic. Furthermore, bioprinting tumor models offers yet another platform for the determination of implant performance and compatibility with ailing tissue^[78]. While 3D bioprinting holds tremendous promise in the creation of functional tissue models and customized implants, several key challenges remain. Some of these challenges are finding biocompatible materials with suitable mechanical and biological properties, providing enough viable cells and maintaining their viability throughout the bioprinting process, creating complex tissues with vascular networks, and addressing the high cost and scalability of 3D bioprinting^[79,80].

Standardization of preclinical animal models

Preclinical animal model standardization is extremely important for the assurance of results reliability and reproducibility in implant dentistry. In the selection of an animal model the anatomy of the animal must closely mimic the intended human population. This encompasses bone density, structure, and the soft tissues covering them. Physiological processes like bone healing and remodeling ought to be akin to those in humans. Susceptibility to infection and the immune response of the animal are also relevant^[10]. The specific application of the implant, i.e., immediate loading or guided bone regeneration, will influence the choice of model. For instance, the implant size and location will influence the animal size and type of bone. Standardization must also be applied to all aspects of the experimental process. Animals need to be subject to comprehensive health screening to establish that they are free from existing conditions that will complicate results. Standardized surgical techniques reduce variability and promote uniform implant positioning and wound closure^[6]. Regular usage of analgesics, antibiotics, and other drugs is mandatory for animal comfort and comparability of data. Standardized tests, such as radiographic examination, histological analysis, and biomechanical analysis, permit objective assessment of implant function. Nonetheless, an exact simulation of human conditions in an animal model is still not available^[1]. Large animal models, though helpful in the assessment of implant occlusion at a macroscopic level, may not be best suited for analysing molecular interactions between the bone/implant interface. Likewise, small animal models such as rodents are frequently selected due to their genetic tractability, but due to their reduced size, they are not so useful for application in some clinical situations. All models possess certain strengths and weaknesses, and choosing the best model involves precise regard for the question of study as well as the particular clinical application^[30].

Ethical challenges and alternatives to animal testing

Ethical issues surrounding animal welfare have compelled the development of alternatives to animal testing for implant research. Computer simulations using advanced software can model the biomechanical performance of implants and their interaction with adjacent tissues, lessening the use of animal studies, especially during the initial stages of development. Cell culture-based laboratory tests and simulated physiological conditions can test biocompatibility, osseointegration capacity, and material and surface modification effects. The growing application of in vitro approaches, especially for the “Big Three” of biocompatibility testing: cytotoxicity, sensitization, and irritation. These sophisticated models, such as organ-on-a-chip systems and 3D-printed tissues, provide a better mimicry of human physiology and disease conditions than conventional cell cultures^[81,82]. Although these alternatives are increasing in popularity, the medical device industry is wary of completely substituting animal models. Biological systems show multifaceted interaction between various cell types, tissues, and organ systems, and it is difficult to duplicate this in full in vitro. Although animal models are not ideal, they do present a more global picture of interactions. Implants are intended to be implanted permanently in the body, and chronic effects of the material and the degradation products have to be tested. The methods available today for in vitro tests are mostly unable to mimic long-term exposure^[53]. The limitation particularly concerning the difficulty in evaluating delayed cytotoxic or inflammatory reactions as a result of material degradation. The data from animal testing are commonly required by regulatory agencies for preclinical safety and efficacy assessments. Although there is an increasing trend towards the acceptance of alternative approaches, regulatory policies are still developing. There continues to be the challenge of balancing the requirement for stringent testing and ethical concerns. Although in vitro approaches are potent tools for the initial screening as well as the study of mechanism^[83], detailed assessments like evaluating systemic toxicity, carcinogenicity, and reproductive/developmental toxicity continue to depend on models using animals. The future of implant testing probably resides in an integration of methodologies, strategically combining in vitro and in silico techniques with improved and minimized animal studies, as promoted by the 3Rs principles (reduction, refinement, and replacement) further supports this paradigm shift towards increased dependence on in vitro techniques and improved risk assessment data and greater confidence in these alternatives^[84]. Table 3 describes the AI-driven predictive models for implant success

Table 3.

AI-driven predictive models for implant success^[85,86]

AI model type	Algorithm used	Application in implant success prediction	Key parameters analyzed	Example use cases
Machine learning (ML) models	Random Forest, SVM, XGBoost	Predicting implant longevity and failure risk	Patient demographics, implant material, surgical technique	Orthopedic implants, dental implants
Deep learning (DL) models	CNN, RNN, LSTM, GANs	Analyzing medical imaging for implant integration	CT/MRI scans, X-rays, histological images	Osseointegration monitoring, early detection of implant rejection
Reinforcement learning (RL) models	Q-learning, DDPG, PPO	Optimizing surgical parameters for implant placement	Real-time sensor data, surgeon input	Robotic-assisted surgery, adaptive implant positioning
Natural language processing (NLP) models	Transformer-based models (BERT, GPT)	Extracting insights from clinical reports and research papers	Electronic health records (EHR), medical literature	Personalized implant recommendations, postoperative risk assessment
Hybrid AI models	Combination of ML, DL, and RL	Integrating multiple data sources for holistic prediction	Genomic data, physiological biomarkers, real-time monitoring	Smart implants, AI-driven diagnostics
Explainable AI (XAI) models	SHAP, LIME, attention mechanisms	Enhancing transparency in AI-driven implant predictions	Feature importance, model interpretability	Regulatory approval, clinician trust in AI predictions
Federated learning models	Secure distributed learning	AI training across multiple hospitals without data sharing	Multi-center patient data, privacy-preserving analytics	Global implant success prediction, collaborative AI research
Bayesian AI models	Bayesian Networks, Probabilistic Graphical Models	Handling uncertainty in implant success prediction	Probabilistic dependencies, prior clinical data	Risk estimation, adaptive treatment strategies
Edge AI models	AI deployed on local devices	Real-time monitoring of implant performance	Sensor data from implants, physiological parameters	Smart orthopedic implants, wearable health monitoring
Multi-omics AI models	AI integrating genomics, proteomics, and metabolomics data	Personalized implant success prediction	Genetic predisposition, metabolic response, inflammatory markers	Precision medicine for implant patients
Digital twin models	AI-driven patient-specific virtual simulations	Predicting implant behavior before surgery	3D anatomical modeling, biomechanical simulations	Virtual implant trials, surgery planning

Future directions and translational roadmap

Personalized animal models for precision medicine

Individualized animal models are important for preclinical orthopedic implant testing. Development of animal models that most accurately represent individual patient anatomy and physiology enables scientists to test implant performance and predict clinical results more effectively. This strategy can result in better implant designs and tailored treatment protocols. Human anatomy and bone structure are vastly different between patients. Variables such as age, sex, genetics, and pre-existing health conditions can affect bone density, quality, and healing potential. Common animal models cannot reflect this variability, and thus predictions of implant performance in certain patient groups may be inaccurate^[72]. Customized animal models, through the replication of patient-specific traits, can offer more realistic information about how an implant will function in an actual clinical environment. This enables researchers to refine implant design and surgical methods to enhance clinical performance. The significance of selecting the correct animal model, since in vitro testing is not informative enough. Selecting the correct animal model is essential^[1]. Research highlights the significance of precise choice of animal models and biomechanical test setups for the evaluation of osseous integration of implants. Considerations such as bone structure, size, and healing rate should guide the choice of animal model for a given orthopedic implant application. The use of rats as models, with particular emphasis on calvarial bone regeneration in defects, and emphasizes the need to select relevant models to the clinical scenario. Individualized animal models would be able to reduce the numbers of animals utilized for preclinical studies to the barest minimum, thereby enhancing the ethics^[6]. Medical imaging technology and 3D printing technology have made it possible to design more realistic and patient-specific animal models. Lattice/cellular structure development expertise and patient-specific implants have been cited in literature. There is the utilization of medical imaging for creating models for 3D printing which closely resembles a patient’s anatomy. These are utilized for carrying out surgical planning as well as preclinical investigation for custom-created implants. Scientists further explored additive manufacturing, popularly known as 3D printing, for utilization in medical treatment, vital to creating custom-made models as well as implants^[87].

Ai-enhanced decision support in implant design

AI can revolutionize implant design by automating complex design work, adapting implant geometry to the individual patient’s needs, and predicting implant function. AI algorithms can review patient-specific factors, biomechanical information, and medical imaging to develop patient-specific implant designs that maximize function while minimizing complications^[88]. Traditional implant design is generally time-consuming and includes manual processes. AI can be utilized to automate such processes, such as the generation of 3D models from medical images, optimization of implant geometry for stress distribution, and generation of patient-specific surgical plans. 3D-printed titanium patient-specific implant production and manufacturing were explored^[89]. The use of AI in maxillofacial prosthesis design and production using CAD/CAM technology. AI can employ biomechanical information and patient anatomy to customize implant geometry for best fit, evenly distributing stress and minimizing complications. The finite element modeling is utilized to analyze jawbone and implant characteristics and the ways in which AI can enhance further implant design. The necessity for maximizing implant design in order to enhance biomechanics and immunological response^[90,91]. The new features of implant design, like surface coatings, incur cost, and therefore surgeons must grasp the fundamental science rationale for design modifications. AI can also forecast the long-term performance of implants by modeling their behaviour under different loading conditions. It can assist in determining the possible failure points and optimize implant designs prior to physical testing, shortening the development cycle and expense. Kurmis points out that AI in orthopedics is viewed as inevitable, yet evidence-based reasoning for implementation is essential^[92,93]. Revilla-León writes about AI models for the prediction of implant success, which have been extremely promising but continue to be under development. AI enables the generation of patient-specific implants customized according to individual needs and anatomical differences. This can enhance the fit, functionality, and patient outcomes overall. Research quotes experience in patient-specific implants and 3D printing. It also discusses the categories of machine learning algorithms that are applied to identify patterns within data, which can be utilized to customize implant geometry. Optimizing the design of an implant and anticipating issues, AI can reduce the risk of complications, including implant loosening, infection, and bone loss. Research identifies the challenge of implant loss prediction as a result of numerous risk factors^[94].

Integration of digital twins for preclinical validation

Digital twins, computerized copies of real implants and surrounding tissue, provide an advanced means of preclinical validation. Through simulation of implant performance in a virtual space, scientists can evaluate implant function under diverse loads, identify areas of possible failure, and optimize implant design prior to physical testing. This process has the potential to eliminate some of the necessity for animal tests and speed development of the next generation of implants^[95]. Digital twins enable scientists to conduct virtual trials and simulations mimicking actual environments. These covers simulating varied loading conditions, like walking, running, or jumping, in order to quantify implant stress and strain. Welch-Phillips states that as technology advances, more clinical uses are being established, holding the promise of application in surgical planning and customizing implants to patients’ unique features. This computer simulation is able to identify potential flaws in the design of an implant before constructing physical models^[96]. Predictive modeling of long-term performance of an implant is possible with digital twins. With parameters such as material strength, bone density, and patient activity level included, scientists can predict the long-term stability and lifetime of an implant. This can be used to optimize implant design and material choice for improved clinical outcomes^[97]. One of the significant ethical and practical advantages of digital twins is the potential for reducing the amount of animal work required for preclinical evaluation. Blanc-Sylvestre discusses pre-clinical models in dental implants and is using the terminology of animal tests as a preliminary to human clinical exposure, traditionally performed on minipigs. By means of virtual trials performed on digital twins, researchers can gain valuable data and refine the design of the implants before subjecting them to animal studies. Digital twins can also be used to create patient-specific models taking into account the anatomical variations and bone properties in an individual. This can lead to the creation of customized implants and surgical plans tailored to an individual patient^[30]. 3D printing of titanium patient-specific implants, their production and fabrication, increase implant fit, function, and long-term survival. Through enabling virtual testing and predictive modeling, digital twins accelerate the development of future implants^[98]. Studies indicate the quality of life with UNCD-coated implantable medical devices. The ability to quickly iterate and refine designs in a virtual environment reduces development time and cost, leading to faster clinical translation of new implant technologies^[99]. Studies dictat the use of 3D printing in creating customized implants and patient-specific instrumentation, which can be combined with digital twin technology. 3D printing enables physical prototypes of an implant to be directly printed from digital twin models, allowing for instant prototyping and testing. Figure 3 illustrates a 3D bioprinter setup, along with the essential properties of bioinks and scaffold materials such as non-toxicity, porosity, mechanical stability, biodegradability, biocompatibility, bioresorbability, and osteoinductivity, which are critical for successful tissue regeneration

Figure 3.

Key properties and fabrication of biomedical implants using 3D printing.

Conclusion

This discussion of animal models in surgical implant research has emphasized their essential role in translational medicine. From genetically modified models to large animal preclinical studies, these models offer key information on implant biocompatibility, function, and long-term durability. The development of these models, in conjunction with advances such as AI predictive models and 3D bioprinting, has greatly improved our capacity to measure implant success and hasten the design of next-generation implants. Yet the field is still beset by challenges, not least of which are ethical considerations and the lack of standardized methods to reduce animal use and maximize translational applicability. In the future, personalized animal models, AI-augmented decision support, and the use of digital twins promise unprecedented potential for precision medicine and clinical adoption of novel implant technologies. Further work to optimize these models and respond to ethical issues will be critical to moving the field forward and benefiting patients in the end. These animal models allow for the investigation of osseointegration and bone regeneration, cardiovascular implant integration and endothelialization, neural implants and bioelectronic interfaces, and soft tissue and wound healing biomaterial assessments. Performance is measured via biomechanical testing and load-bearing analysis, histopathological and inflammatory response evaluation, imaging methods for real-time implant localization, and implant stability and degradation studies over extended periods. The area needs to steer through preclinical animal model standardization, animal testing ethics and alternatives, translational hurdles in clinical implant validation, and worldwide regulatory mechanisms for implant approval. Future research must emphasize individualized animal models for precision medicine, decision support using AI in implant design, incorporation of digital twins for preclinical verification, and accelerating clinical use of next-generation implants. In addition, the choice of a suitable animal model is crucial for effective translation of research data to the clinical setting. Considerations such as the particular research question, nature of the implant under investigation, and the clinical outcome desired all play a role in determining the model. Although small animal models, e.g., rodents, are cost-effective and easily manipulated genetically, larger animal models, e.g., minipigs and dogs, can better represent human physiology and provide the ability to test implant performance in a more clinically representative environment. Furthermore, the growing availability of genetically modified and humanized animal models offers new possibilities for investigating particular disease mechanisms and assessing implant performance in specific patient groups. The continued development and improvement of in vitro and in silico models, as well as advances in imaging and bioprinting, also present exciting alternatives to conventional animal testing, further supporting the 3Rs principles of replacement, reduction, and refinement.

Ethical approval

Ethics approval was not required for this review.

Consent

Informed consent was not required for this review.

Sources of funding

No funding received.

Author contributions

V.S.: conceptualization, writing original draft; S.S.I.: visualization, formal analysis; A.S.: writing original draft, resources; S.B.B.: data curation, writing original draft; G.G.: supervision, writing review and editing; S.A.: writing review and editing, visualization; H.C.: supervision, writing review and editing.

Conflicts of interest disclosure

Authors declare no conflict of interest.

Guarantor

Hitesh Chopra.

Research registration unique identifying number (UIN)

Not applicable.

Provenance and peer review

Not commissioned, externally peer-reviewed.

Data availability statement

No new data sets generated.