Artificial Intelligence in the synthesis and application of advanced dental biomaterials: a narrative review of probabilities and challenges

1.1 Biomaterials in dentistry

Biomaterials play a crucial role in tissue engineering [1], 2] and are indispensable across all dental disciplines. It would not be wrong to say that dental biomaterials are as important to the dentist as pharmacology is to the physician. They are used in fillings, crowns, bridges, dentures, implants, orthodontic appliances, root canal treatments, and periodontic treatments [3]. The primary goal of these materials is to replicate or enhance the natural structures of teeth and surrounding tissues while ensuring durability, strength, and biocompatibility under the complex conditions of the oral environment [4].

In restorative and prosthetic dentistry, resin composites and ceramics face issues like polymerization shrinkage, mechanical degradation [5], and brittleness [6], compromising longevity and marginal integrity. Periodontics relies on bioactive materials (e.g., bioactive glasses) for tissue regeneration [7], [8], [9], yet their efficacy in promoting predictable healing remains inconsistent. Orthodontics employs brackets, wires, and adhesives, which risk enamel demineralization [10], 11] and bond failure due to mechanical fatigue or biofilm accumulation [10], 11]. Endodontics depends on root canal sealers and gutta-percha, which may exhibit inadequate sealing or biocompatibility, increasing reinfection risks [12]. Preventive dentistry utilizes pit-and-fissure sealants and fluoride-releasing materials [13], 14], though their durability and sustained therapeutic effects are limited. Across these disciplines, persistent issues such as biofilm adhesion, material fatigue, interfacial debonding, and biocompatibility challenges demand innovative solutions to enhance clinical performance and patient outcomes.

1.2 Traditional approaches for the synthesis and optimization of dental biomaterials

Conventional strategies for developing dental biomaterials rely on empirical methods, systematic testing, and material science principles. These approaches combine trial-and-error with expert knowledge to refine performance [15], [16], [17]. Key aspects include:

1. Empirical formulation: Resin composites and related materials are systematically improved by testing different resin, filler, and additive combinations using empirical formulas that help quantify their properties. This approach enables researchers to assess and refine key characteristics such as strength, wear resistance, handling, and aesthetics through calculated measurements and standardized testing methods. The data derived from these tests allow for the prediction of clinical behavior, guide iterative formulation improvements, and optimize material performance in real-world dental applications. Multiple rounds of such refinement based on empirical testing and quantitative analysis are typically required to achieve the desired balance of mechanical and biological properties for clinical success [18], [19], [20].

2. Filler and composition optimization: In glass-ionomers and zirconia ceramics, properties are tuned by modifying filler type, particle size, stabilizers, or processing. Zirconia’s fracture resistance and translucency depend on sintering and alloying, while glass-ionomer performance is governed by the glass–polyacid ratio [21].

3. Surface treatments: Titanium alloys and bioactive glasses benefit from treatments such as sandblasting, acid etching, or anodizing, which improve surface roughness, osseointegration, and bioactivity. These modifications are optimized through testing to achieve desired biological and mechanical outcomes [22].

4. Polymer and additive selection: Hydrogels and other polymer-based biomaterials are tailored by choosing suitable polymers (e.g., PVA, chitosan, collagen) and adjusting crosslinking or concentration to balance biodegradability, elasticity, and biocompatibility [23], 24].

5. In vitro testing: Optimization traditionally involves mechanical and durability assessments (tensile, compressive, flexural, wear, and fatigue) under simulated oral conditions, including thermocycling, mechanical loading, and acidic exposure, to predict clinical performance.

Traditional approaches, while effective, are time-consuming, costly, and limited in predicting long-term behavior or nanoscale interactions [16], 25]. Consequently, emerging technologies such as Artificial Intelligence and computational modeling are increasingly being adopted to complement and enhance these methods, enabling greater precision and efficiency in the design and optimization of advanced dental biomaterials.

2.1 Overview of AI technologies

Machine Learning (ML): A subset of AI, ML enables systems to learn from data, identify patterns, and make predictions without explicit instructions [31]. This ability to predict and classify outcomes from large volumes of data makes ML particularly valuable in industries like healthcare, manufacturing, and more [32]. In its application, ML involves supervised learning (where algorithms are trained on labeled data), unsupervised learning (where the system identifies patterns without labeled inputs), and reinforcement learning (where systems learn through trial and error) [33], 34]. In medical imaging, ML is used to classify images as cancerous or non-cancerous [35], a task similarly being applied in fields like dental imaging for diagnosing conditions.

Deep Learning (DL): DL, a more advanced subset of ML, uses artificial neural networks (ANNs) with many layers (hence the term “deep”) to model complex patterns in data [31], 33]. These networks simulate the way the human brain processes information and are particularly powerful when working with unstructured data like images, audio, and text [36]. DL systems can automatically extract relevant features from raw data, eliminating the need for manual feature engineering, which makes them particularly useful in fields with large volumes of data [36]. In healthcare, DL is applied to interpret patient records, clinical notes, and research articles, facilitating the extraction of useful insights from unstructured data [37], 38].

Optimization algorithms: These AI methods systematically identify the optimal solutions from a set of feasible alternatives [39] and are extensively applied in domains such as material synthesis, logistics, and scheduling, where the complexity of decision variables necessitates advanced computational approaches [40]. Their utility is particularly evident in the analysis and management of systems characterized by multiple interacting parameters, enabling efficient, accurate decision-making [41]. Within healthcare, optimization algorithms contribute significantly to resource allocation, treatment planning, and diagnostic procedures [42]. Figure 1 provides an overview of the paradigm shift from empirical, experience-based approaches to AI-driven methodologies in dental biomaterials research, emphasizing the integration of ML, DL, and computational optimization strategies.

Figure 1:

Evolution of methodologies in dental biomaterials research. The schematic illustrates the transition from traditional, empirical approaches to modern, data-driven Artificial Intelligence (AI) methodologies. AI encompasses key branches such as ML for predictive modeling, DL for complex pattern recognition in images and data, and optimization algorithms for efficiently identifying ideal material compositions and structures.

While the role of AI in dentistry is well-established in diagnostic and therapeutic domains [43], [44], [45], its application specifically to the synthesis and utilization of dental biomaterials remains an emerging field. AI technologies have seen widespread adoption in areas such as oral radiology [46], orthodontics [47], endodontics [48] and implantology [49], 50]. However, the integration of AI in dental biomaterials research is still limited but holds considerable promise. This paradigm shift enables the development of advanced biomaterials with optimized physicochemical properties, enhanced predictive performance, and potential for personalized clinical application. Consequently, AI is marking a transition toward data-driven innovation within dental materials science, opening new avenues for material design and functional optimization.

This review provides a critical evaluation of the role of AI in the synthesis, optimization, and clinical performance of dental biomaterials. An electronic search of Google Scholar, Scopus, PubMed, and Web of Science was conducted using the terms “Artificial Intelligence in biomaterials”, “machine learning in biomaterials”, “deep learning in biomaterials”, “optimization algorithms in biomaterials”, and “neural networks in biomaterials”. Only peer-reviewed, English-language articles indexed in major databases were included. This review follows a narrative synthesis approach and does not adhere to PRISMA systematic review guidelines. The review synthesizes current research on AI-driven methodologies – including ML, DL, and computational optimization – to address challenges in material performance (e.g., mechanical durability, polymerization shrinkage), biocompatibility, and longevity. It further explores AI’s potential to accelerate the discovery of advanced dental biomaterials, enhance predictive modeling under oral conditions, and enable personalized, patient-specific solutions. Finally, it highlights the latest literature on AI-driven materials discovery, predictive modeling, while pinpointing gaps and proposing new directions for interdisciplinary research. This review does not involve original modeling or numerical simulations; instead, it synthesizes methodologies from published studies. Unlike previous reviews, this article integrates both traditional and emerging AI frameworks to create a comprehensive perspective on their impact and potential in dental biomaterials research.

3.1 ML in dental biomaterials synthesis and application

ML facilitates predictive modeling by identifying patterns within large datasets, enabling the estimation of crucial material properties such as mechanical strength, polymerization shrinkage, and biocompatibility [51]. Algorithms including Support Vector Machines (SVMs), Random Forests, ANNs, and k-nearest neighbors (KNN) have been employed to optimize formulation processes and reduce experimental costs [16], 52]. SVMs function by classifying data through determination of optimal hyperplanes that separate distinct material property classes, while Random Forests leverage an ensemble of Decision Trees to enhance predictive accuracy [16]. ANNs, inspired by biological neural networks, comprise interconnected layers of nodes or neurons that process complex information, recognize patterns, and generate predictions. This approach is particularly beneficial for elucidating complex relationships between material components and their resultant properties [51]. Collectively, these ML models offer powerful tools for rapid evaluation and optimization of advanced dental biomaterials, thereby accelerating research and development in restorative and prosthetic dental applications.

ML can support resin composite optimization by determining resin–filler ratios for targeted mechanical performance, predicting biological responses, and evaluating durability under stress, aging, or environmental challenges. It can also guide strategies for surface modification to improve bonding or osseointegration and facilitate virtual screening for bioactive or remineralizing materials.

In a study predicting resin composite performance, ML models utilized data from 233 commercial resin composites to identify key factors influencing flexural modulus and volumetric shrinkage. The k-nearest neighbors (KNN) model, a widely utilized supervised ML algorithm for classification and regression tasks [53], demonstrated 90 % predictive accuracy for flexural modulus, highlighting triethylene glycol dimethacrylate (TEGDMA), a monomer that enhances rigidity through crosslinking, as the most influential factor (39 % importance). Similarly, a Decision Tree model predicted volumetric shrinkage with 81 % accuracy. This model partitions the data based on material properties such as resin type, filler content, and curing method, thereby elucidating how these features affect shrinkage behavior. It revealed that increased TEGDMA content elevates shrinkage due to its reactive chemistry, while greater filler loading reduces shrinkage by diluting the resin matrix [54].

Designing ceramics with enhanced properties presents significant challenges due to the vast array of elemental combinations. In one study, ML was employed to predict optimal mixtures for high-entropy ceramics (HECs) composed of equal parts of transition metals such as hafnium, niobium, tantalum, titanium, and zirconium. A Random Forest algorithm, an ensemble method that integrates multiple decision trees, was utilized to improve prediction accuracy and robustness [55]. The model analyzed data from existing ceramics, considering factors such as atomic size differences and thermodynamic stability, to identify compositions likely to form stable, single-phase ceramics. The predictive performance facilitated the identification of 70 new HEC formulas, which were later validated experimentally. These ceramics showed uniform microstructures and higher physical and mechanical properties [56]. While the study focused on general biomedical applications, the same ML approach can be adapted for the synthesis of advanced dental ceramics (e.g., zirconia or bioactive glass).

In another study, the investigators proposed an ML-driven approach utilizing a large database of material structures analyzed through finite element analysis (FEA). A self-learning algorithm refined those structures by eliminating weaker designs and selecting high-performing candidates. The results demonstrated that the method can generate tougher and stronger materials, validated through additive manufacturing. Additionally, ML served as an efficient alternative to conventional microstructural analysis, significantly enhancing computational efficiency. This smart additive manufacturing approach paved the way for the accelerated discovery and fabrication of novel high-performance materials [57]. Similarly, ML can optimize ceramic-resin composites for dental crowns to balance strength and aesthetics or fine-tune printing settings for precise dental implants. By automating trial-and-error, ML can rapidly develop materials tailored for dental applications, such as patient-specific bridges with ideal fit and durability, reducing clinical failure rates.

Subsequently, Wang et al. demonstrated the use of ML to predict the µTBS of dental adhesives, reducing reliance on time-intensive laboratory testing. Data from 81 adhesives were analyzed using nine ML algorithms, identifying organophosphate monomer (i.e., MDP), pH, organic solvent, and 2-hydroxyethyl methacrylate as key factors influencing µTBS. A refined model using these four features achieved an AUC of 0.90 and an accuracy of 0.81. The results highlighted ML’s potential to accelerate the discovery and optimization of dental adhesives, enhancing efficiency in materials research [58].

While these ML methods have significantly improved predictive capabilities, more complex phenomena such as aging, dynamic degradation, and environment-responsive behavior require more sophisticated modeling frameworks such as DL and optimization algorithms.

3.2 DL in dental biomaterials synthesis and application

DL is a subset of ML that utilizes ANNs with multiple layers to model complex patterns and relationships in data [59], 60]. DL can gain significant attention in dental biomaterials research due to its ability to process large datasets, recognize intricate patterns, and optimize material properties without extensive trial-and-error experimentation. DL techniques are especially valuable in predicting the mechanical, physical, chemical, and biological behavior of dental materials, streamlining material development and enhancing clinical applications.

Park et al. used DL models, specifically YOLO (You Only Look Once), a popular real-time object detection algorithm, and SSD (Single Shot Detector), which detects objects in a single pass – to automate cephalometric landmark detection on lateral cephalograms. YOLO achieved 5 % higher accuracy than SSD in their study. This advancement can enhance the design of orthodontic appliances (e.g., aligners, brackets) by ensuring precise force distribution and geometric customization based on biomaterial properties, such as the flexibility of thermoplastic polymers or the durability of ceramic brackets. The study highlighted YOLO’s efficiency in processing high-resolution images (608 × 608 pixels), which reduces manual errors in appliance fabrication workflows [61]. However, limitations included reliance on a single examiner for validation and the need for larger datasets to generalize results across diverse patient anatomies.

Lee et al. applied Convolutional Neural Networks (CNNs) – designed for image analysis through spatial feature-learning convolutional layers – to detect caries in periapical radiographs with 89 % accuracy. This approach can also be adapted to monitor degradation in resin composites or adhesives. By analyzing marginal leakage or secondary caries around restorations, DL models can predict material failure caused by moisture exposure or mechanical stress [62]. Similarly, Casalegno et al. utilized CNNs to evaluate near-infrared transillumination (NILT) images, achieving an AUC of 85.6 % for proximal lesions, a technique extendable to identifying subsurface cracks in resin composites [63]. Challenges include low-resolution training data (e.g., 256 × 320 pixels in Casalegno et al.) and the lack of longitudinal studies to correlate radiographic patterns with long-term biomaterial performance.

3.3 Optimization algorithms in dental biomaterials synthesis and application

Optimization algorithms are computational tools that can refine material composition by adjusting polymer-filler ratios, processing conditions, and chemical formulations. In dental biomaterials, these algorithms can be critical for tailoring materials like polymers, ceramics, resin composites, and alloys to a clinical need. The most commonly used optimization algorithms include.

3.3.3 Topology optimization

Topology Optimization is widely used in designing dental prosthetics and biomaterials to maximize mechanical efficiency and conserve material [70]. By identifying optimal configurations within a design space, it improves strength-to-weight ratios [71]. In dentistry, it can aid in customizing denture frameworks, implants, and crowns through FEA, enhancing load-bearing performance and patient comfort by minimizing bulk.

A recent study used molecular docking and dynamics simulations to optimize resin composites by assessing interactions among monomers, fillers, and coupling agents. The method predicted binding energies and mechanical properties, with EBPADMA-SiO₂-TRIS showing balanced performance and clinical promise. This highlights the value of in silico tools for designing and optimizing dental biomaterials before experimental validation [72].

Beyond property prediction, AI is increasingly applied to optimize material compositions using algorithms like GA and Particle Swarm Optimization (PSO) [73], which mimic natural processes to refine ingredient ratios and processing parameters. GA can optimize filler-to-resin ratios in composites, while PSO can adjust ceramic sintering for desired translucency and strength. These methods speed up material discovery by narrowing experimental trials, with key techniques summarized in Figure 3.

Figure 3:

Key AI techniques for the design of dental biomaterials. The diagram categorizes core AI methodologies and their primary applications: ML for predicting material properties and optimizing formulations; DL for analyzing complex imaging and microstructural data; and optimization algorithms for navigating multi-variable parameter spaces to discover ideal material compositions and processing conditions.

4.1 Data quality and availability

A major barrier to AI in dental biomaterials is the scarcity of high-quality, standardized datasets [75]. Data are fragmented across institutions, clinics, and industry, often in inconsistent formats, hindering aggregation and analysis. Experimental data for novel biomaterials, like bioactive glasses or high-entropy ceramics, are limited due to costly and time-consuming synthesis. Small datasets, such as the 81 adhesives analyzed by Wang et al. [58], risk overfitting and reduce model reliability. Labeling data for AI training, e.g., correlating micro-CT scans with fracture resistance, is labor-intensive and subjective. Proprietary industry data further limit diversity for academic research, while the complex structures of biomaterials demand ML models capable of capturing detailed, accurate insights [18].

4.2 Generalizability of AI models

AI models trained on narrow datasets often fail to generalize across materials or clinical scenarios [76]. Models optimized for one material class (e.g., resin composites) may not translate to others (e.g., zirconia ceramics). Therefore, variations in chemical composition, fabrication techniques, and degradation pathways necessitate material-specific retraining. Models validated in controlled laboratory settings may underperform in real-world environments due to factors like salivary pH fluctuations, occlusal forces, or patient-specific biofilm communities. Additionally, the predictive models based on accelerated aging tests (e.g., thermocycling) may not fully capture long-term clinical outcomes, such as the interfacial debonding of implants under dynamic loading.

4.3 Computational complexity and cost

The resource-intensive nature of AI workflows poses practical barriers. Training DL models (e.g., CNNs for microstructural analysis) demands high-performance GPUs, which are cost-prohibitive for many dental research labs. For example, topology optimization of scaffold designs requires extensive finite element simulations, increasing computational overhead. Since dental clinicians/researchers often lack computational skills to implement advanced AI techniques like Bayesian optimization or GA, necessitating interdisciplinary collaboration. Additionally, large-scale AI training consumes significant energy, raising sustainability concerns [74].

4.4 Lack of standardization

The lack of standardized frameworks hinders reproducibility and benchmarking in AI research. Variability in protocols, data formats, and methodologies complicates model training and generalization [17]. Algorithms are often chosen arbitrarily – e.g., SVMs or ANNs – without proper justification or benchmarking, limiting comparability across studies [77], 78]. Diverse reported metrics (accuracy, AUC, F1-score) and incomplete details on training data or hyperparameters further restrict replication [79], 80]. FAIR principles (Findable, Accessible, Interoperable, Reusable) are rarely applied in dental biomaterials AI research, exacerbating these issues [25], 81]. Addressing these challenges requires collaborative efforts to establish open-data repositories, validate models in diverse clinical settings, and adopt standardized reporting guidelines. Interdisciplinary collaboration between materials science, dentistry, and computer science is essential to translate AI-driven innovations from conceptual potential to tangible clinical impact.

5.1 Integration with emerging technologies

AI integration with advanced manufacturing and digital workflows will transform dental biomaterial innovation. It can optimize printing parameters for zirconia, resins, and bioactive ceramics, reducing defects and enhancing strength. Reinforcement learning can adjust laser power in selective laser sintering for lattice implants, while generative design and topology optimization can enable patient-specific crowns and bridges. AI-powered 3D printing can produce gradient-index composites mimicking enamel, and virtual simulations using sensor data can allow preemptive formulation adjustments. Augmented Reality overlays of predicted material performance can guide clinicians in selecting optimal biomaterials.

5.2 Enhancing predictive models for clinical applications

To maximize AI’s potential in dental biomaterials, predictive models must move beyond controlled labs and reflect the complexity of the oral cavity. Future systems should be context-aware, integrating multimodal data across three domains: clinical parameters (e.g., salivary pH, occlusal loading, bruxism), imaging data (CBCT, micro-CT, OCT), and biological profiles (microbiome, immune responses, genetics). This integration allows patient-specific predictions of material behavior. For instance, combining microbiome data with bite force can help estimate resin composite degradation and restoration wear in patients with acidic environments and heavy masticatory loads.

5.3 AI for personalized dental biomaterials

Artificial Intelligence is paving the way for a new era in dentistry in which biomaterials are no longer standardized but are instead tailored to the unique biological and behavioral profiles of individual patients. This personalized approach has the potential to significantly enhance material longevity, biocompatibility, and therapeutic outcomes.

5.4 AI in the development of smart and sustainable dental biomaterials

Beyond clinical functionality, the future of dental biomaterials lies in sustainability and multifunctionality. AI can synthesize and formulate next-generation dental biomaterials that are not only smart but also sustainable, eco-conscious, and resource-efficient. AI-powered generative design tools can engineer polymer networks with dynamic covalent chemistry, such as Diels-Alder or disulfide bonds that facilitate autonomous healing of microcracks caused by masticatory fatigue or thermal cycling. Furthermore, AI can optimize polymer recycling processes for recovering monomers from used dental restorations, facilitating circular recycling of temporary crowns, dentures, and aligners, thereby contributing to the reduction of clinical plastic waste and promoting sustainability in dental practice.

Inspired by nature, AI can simulate molecular interactions to identify renewable, plant-based alternatives to petroleum-derived dental polymers. For instance, lignin-derived epoxy systems or alginate-based scaffolds can be computationally optimized for adhesion, mechanical strength, and biocompatibility. Additionally, algae-derived hydroxyapatite and cellulose nanofibers offer promising avenues for green restorative and bone-regenerative materials, with AI helping refine their structural and surface properties for enhanced integration with oral tissues.

5.4.2 AI-designed antimicrobial nanomaterials

Reinforcement learning algorithms can iteratively optimize nanomaterial properties such as size, charge density, and surface chemistry to selectively target cariogenic or periopathogenic biofilms. Nanoparticles like silver, zinc oxide, or graphene derivatives can be fine-tuned for maximal antimicrobial efficacy while ensuring low cytotoxicity to oral fibroblasts or pulp cells. These smart nanoparticles can be embedded in adhesives, liners, or temporary restorations to provide sustained, localized antimicrobial action.

The future of dental biomaterials lies at the intersection of AI, personalization, and sustainability. By harnessing AI’s predictive power alongside additive manufacturing and smart material technologies, researchers can develop biomaterials that not only restore function but also actively promote oral health and ecological balance. Collaborative frameworks – uniting dentists, data scientists, and environmental engineers – will be critical to translating these innovations into clinical practice, ensuring equitable access and global scalability. The broad applicability of AI across various divisions of dental biomaterials is depicted in Figure 4.

Figure 4:

Potential applications of AI-driven design across various domains of dental biomaterials.