Research Progress in Artificial Intelligence-Assisted Preparation of High-Quality Biomaterials

Abstract

The field of biomaterials development is undergoing a fundamental paradigm shift, moving from empirical, trial-and-error approaches to data-driven, intelligent design strategies powered by Artificial Intelligence (AI). This review systematically synthesizes recent progress in applying AI and Machine Learning (ML) technologies to the preparation of high-quality biomaterials. It begins by outlining core AI methodologiesincluding foundational learning paradigms and advanced architectures such as Graph Neural Networks (GNNs) and Transformersand discusses their alignment with specific types of biomaterials data. The article then details AI’s transformative role across three critical stages of the biomaterials R&D pipeline: (1) precision prediction of properties via high-throughput screening and virtual data analysis; (2) inverse design driven by target performance requirements; and (3) rapid multiobjective optimization of both material formulations and synthesis process parameters. Illustrative case studies demonstrate how these AI-enhanced approaches significantly accelerate design efficiency, expand discovery space, and foster innovation. Furthermore, the review critically examines persistent challenges, such as data scarcity and heterogeneity, model interpretability and reliability, rigor in validation, and ethical-regulatory concerns. Finally, we present a forward-looking perspective on emerging directions, including the evolution toward autonomous intelligent design, end-to-end smart manufacturing, cross-disciplinary integrated applications, and a transition to sustainable development. The deep integration of AI is positioned to fundamentally accelerate the discovery, optimization, and clinical translation of next-generation, high-performance biomaterials for regenerative and precision medicine.

1. Introduction

Artificial Intelligence (AI), as a disruptive cutting-edge technology, is reshaping various industries with unprecedented depth and breadth and has become a core driver of global technological innovation and industrial upgrading. , Its development history has been tumultuous. Since its establishment as a discipline at the 1956 Dartmouth Conference, it has experienced the rise and fall of symbolic reasoning to the remarkable rise of machine learning (particularly deep learning). , Benefiting from the rapid development of computational hardware (e.g., GPUs), the advent of the big data era, and revolutionary breakthroughs in algorithmic theory (e.g., the success of AlexNet in the 2012 ImageNet competition), AI is witnessing its third wave of development. It has rapidly moved from the laboratory to industrial applications, achieving milestone successes in fields such as computer vision, natural language processing, and intelligent decision-making, profoundly altering the way humans interact with technology, for instance, in visual perception, speech recognition, decision-making, and language translation. Particularly in the past decade, with breakthroughs in deep learning algorithms, the accumulation of large-scale data, and significant improvements in computational power, AI technology has developed rapidly. Landmark events like AlphaGo (2016) and ChatGPT (2022) have captured global attention, establishing AI as a core force driving interdisciplinary convergence and industrial transformation. Artificial intelligence systems can be trained on large data sets for prediction, object classification, and performing other complex tasks. , Machine learning is a frontier technology derived from AI development, constituting a multidisciplinary field that combines computer science, mathematics, philosophy, control theory, determinism, and other disciplines. − With the rise of AI technology, biomaterials design has entered a new intelligent phase. Around 2010, machine learning methods began to be preliminarily used for predicting material properties and biocompatibility. In 2019, Li et al. established a predictive model for the hydrogel-forming ability of peptides by integrating combinatorial peptide libraries with machine learning, marking a breakthrough application of AI in biomaterials design. Subsequently, more advanced AI methodssuch as Artificial Neural Networks (ANN), Generative Adversarial Networks (GAN), Graph Neural Networks (GNN), and Large Language Models (LLM)have been widely introduced. These technologies can not only efficiently predict material functions and optimize synthesis pathways but also uncover complex mapping relationships between “composition-structure–property,” promoting the shift in biomaterials R&D from the traditional “experience-driven” paradigm to a new, intelligent “data-driven” paradigm. For example, in data analysis, Takahashi et al. utilized unsupervised machine learning to accurately estimate survival probability in nonsmall cell lung cancer by analyzing six multiomics data sets from The Cancer Genome Atlas, identifying new survival-related subtypes. In clinical applications. Particularly noteworthy, the success of AI in broader materials science provides a powerful reference for the biomaterials field. For instance, generative models assist in designing functional peptides and polymers; graph neural networks predict biodegradability and biocompatibility; multimodal learning integrates spectral, microscopic imaging, and bioactivity data to achieve systematic evaluation and inverse design of material properties. The combination of AI with high-throughput experimentation − is building an efficient closed-loop R&D system of “design-synthesis-characterization-optimization,” greatly accelerating the development of biomaterials for personalized medicine. Recent cutting-edge research vividly exemplifies this transformative power of AI in biomaterials research. For instance, a specialized deep learning model has been developed to predict and optimize ionizable lipids for mRNA delivery, successfully identifying a novel lipid (YK-407) with superior spleen-targeting specificity and antitumor efficacy compared to clinically approved benchmarks, thereby demonstrating AI’s capability to solve complex organ-selective delivery challenges through inverse design. In another pioneering paradigm, a BERT-based framework achieved approximately 20% higher accuracy than traditional machine learning models in screening potent antioxidant compounds from an extensive library of natural herbal constituents; the AI-identified candidates were then effectively integrated into a liposomal delivery platform, which validated their enhanced bioavailability and therapeutic performance in mitigating oxidative stress injury in vivoshowcasing a seamless “intelligent discovery-to-efficient delivery” pipeline. These targeted advances, complemented by comprehensive reviews that systematically catalogue AI applications across polymeric, metallic, ceramic, and composite biomaterialsspanning property prediction, structural generation, and process optimizationcollectively underscore a definitive paradigm shift from experience-dependent, trial-and-error approaches to a predictive, data-driven science. Traditionally, biomaterials development heavily relied on the “trial-and-error” method and empirical intuition, which is time-consuming, costly, and struggles to meet multiobjective optimization needs. The intervention of AI aims to transform this process from an “art” into a “science,” achieving a paradigm shift from “experience-guided discovery” to “theoretical prediction followed by experimental verification.”

Therefore, the deep integration of artificial intelligence (AI) with the preparation of high-quality biomaterials represents not merely the adoption of a novel tool, but a fundamental paradigm shift in the field’s research and development ethos. The traditional, experience-driven “trial-and-error” approach is increasingly inadequate to navigate the exponentially complex design space defined by the intricate, nonlinear relationships between a biomaterial’s composition, structure, properties, and processing parameters. , This complexity is compounded by the stringent, multiobjective requirements for clinical successbalancing superior biocompatibility, tailored mechanical and degradation profiles, and specific biofunctionality. AI, particularly machine learning, emerges as the pivotal engine to transition from this empirical art to a predictive, data-driven science. By establishing accurate “composition-structure–property” mappings, enabling target-performance-oriented inverse design, and orchestrating rapid multiparameter optimization, AI provides an unprecedented capacity to systematically explore vast combinatorial possibilities. This intelligent, closed-loop “design-prediction-optimization” framework efficiently identifies Pareto-optimal solutions that reconcile competing material objectives, thereby operationally defining and delivering “high-quality.” Ultimately, this convergence dramatically accelerates the innovation cycle, slashes development costs, and paves a reliable path toward clinically translatable, patient-specific biomaterialsfrom smart drug delivery carriers and bioactive implants to engineered tissue scaffoldspositioning AI as the cornerstone for the next generation of regenerative and precision medicine. −

To construct a systematic and clear knowledge framework, this review will adhere to the following structure. Chapter 1, the Introduction, establishes the research background, significance, and scope. Chapter 2 (Foundations of Artificial Intelligence Methodologies) will provide an in-depth analysis of the core computational paradigms underpinning the field: supervised learning (e.g., neural networks for property prediction), unsupervised learning (e.g., clustering algorithms for material classification or dimensionality reduction), and reinforcement learning (optimization strategies for sequence or structure design). It will also highlight several advanced model architectures that have shown great potential in biomaterials research, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), and Transformer models. Chapter 3 (Applications of AI in Biomaterials Development), the core of this review, will elaborate from a holistic “design-optimization-synthesis” perspective on the revolutionary role of AI in three key areas: (1) accurate prediction of material properties through AI-assisted virtual screening, enabling efficient identification of target materials from extensive candidate spaces; (2) inverse design of biomaterials, “deriving” ideal material compositions or structures based on specified performance requirements; (3) rapid multiobjective optimization and synthesis control, encompassing compositional tuning, and intelligent adjustment of process parameters to ensure reproducible and efficient synthesis. Finally, Chapter 4 (Outlooks and Challenges) will discuss the core challenges the field faces toward maturity, including data quality and sharing, model interpretability, cross-scale modeling, and ultimate clinical translation, and provide a strategic outlook on its future directions. Through this progressive discursive system from theory to practice, this review aims to provide readers with a comprehensive and profound understanding, facilitating research in AI-assisted biomaterials development.

2. Defining High-Quality Biomaterials: A Multidimensional Paradigm for the AI Era

The paradigm of biomaterials development, now profoundly reshaped by artificial intelligence, necessitates a redefinition of what constitutes “high quality.” This concept has evolved from a pursuit of singular properties into a complex, systems-level engineering goal. − It describes an advanced material system engineered to engage in precise, controllable, and efficient dialogue with biological systems to successfully execute predefined clinical functions. Its excellence is not a static attribute but a dynamic equilibrium achieved across the entire lifecyclefrom computational design to clinical degradationbalancing five interconnected dimensions that are themselves being transformed by data-driven approaches.

A fundamental dimension is the material‘s dynamic and proactive biointerfacial performance. Moving beyond passive biocompatibility, high-quality biomaterials exhibit active biointegration. Their surface chemistry and topography are designed to orchestrate the host immune response, for instance by steering macrophage polarization toward a pro-regenerative phenotype to mitigate chronic inflammation and fibrous encapsulation. Simultaneously, these materials dynamically interact with cellular processes, facilitating specific protein adsorption, integrin binding, and the spatiotemporal delivery of biological cues to direct cell fateadhesion, proliferation, differentiationand thereby sculpt a regenerative microenvironment. −

The material’s mechanical and degradation behaviors must be biomimetic, adaptive, and temporally synchronized with healing. Its initial mechanical propertiesstiffness, strength, toughnessrequire precise matching with the native tissue’s biomechanical environment to prevent stress-shielding or mechanical failure. For biodegradable materials, this is coupled with a critical requirement: their degradation kinetics must be spatiotemporally coupled with the tissue regeneration timeline. The scaffold must provide stable support initially, then undergo controlled resorption where its strength decay curve seamlessly intersects the strength increase of the newly formed tissue, with all byproducts being metabolically safe, enabling a complete transition from artificial support to native tissue.

Furthermore, high-quality biomaterials are structurally intelligent carriers of function. Through advanced fabrication like 3D/4D printing, they embody precise, multiscale architecturessuch as gradient porosity, microchannels, or nanofibrous networksthat physically guide biological processes like cell migration, axonal growth, and vascularization. − Beyond passive structure, they can be endowed with environmental responsiveness, reacting to specific pathological triggers (e.g., pH, enzymes) or external stimuli (e.g., light, magnetic field) to execute functions like on-demand drug release, shape morphing, or property switching, enabling combined diagnostic and therapeutic roles. −

This sophistication necessitates that “high-quality” also defines the manufacturing process itself. It relies on a closed-loop, data-driven pipeline of design, prediction, and fabrication. AI models mine existing data to predict how composition and process parameters dictate final properties. Generative models then use these predictions for inverse design, proposing novel material formulas that meet multiobjective requirements. Finally, machine learning controls and optimizes fabrication parameters in real-time during processes like bioprinting, ensuring the reproducibility, scalability, and consistency essential for translating lab-scale innovation into reliable clinical-grade products.

Ultimately, all dimensions converge on and are disciplined by the imperative of clinical translation. True quality is inherently linked to regulatory and manufacturing rigor. The development pathway must be aligned with regulatory science, encompassing standardized biocompatibility testing (e.g., ISO 10993), efficacy validation in relevant disease models, and manufacturing under Quality-by-Design (QbD) and Good Manufacturing Practice (GMP) frameworks. The goal is to generate a comprehensive, auditable evidence trail that satisfies the stringent reviews of agencies like the FDA or NMPA, ensuring that the material’s sophisticated design translates into a safe, effective, and commercially viable therapeutic product. −

In essence, a high-quality biomaterial represents the optimal solution to a profoundly complex, multiobjective optimization problem, where advancing one property (e.g., bioactivity) often compromises another (e.g., immunogenicity). − This high-dimensional design space is intractable to traditional empirical methods. Artificial intelligence, particularly through integrated platforms of predictive modeling, generative design, and automated experimentation, serves as the essential tool to navigate this space. It systematically explores the vast combinatorial universe of chemistry and processing to identify Pareto-optimal solutions, thereby transforming biomaterials science from an experience-driven art into a predictive, precision engineering discipline. − This shift is pivotal for developing the next generation of intelligent materials capable of sophisticated, personalized interactions within the human body.

3. Materials and Methods

3.1. Literature Search

This review focuses on the transformative role of Artificial Intelligence (AI) in the preparation and development of high-quality biomaterials. Our objective is to synthesize recent progress in the application of data-driven methods, including Machine Learning (ML) and Deep Learning (DL), to innovate biomaterial design, property prediction, and fabrication processes. −

The scope of this review encompasses: (1) precision prediction of biomaterial properties (e.g., biocompatibility, mechanical performance): using AI models; (2) target-performance-driven inverse design of novel material compositions or structures; (3) rapid optimization of both material formulations and preparation process parameters; (4) discussion of foundational AI methodologies, current challenges, and future outlooks in this field.

A systematic literature search was conducted from July to August 2025 across four core databases: Web of Science, Scopus, PubMed. Supplementary searches were performed on arXiv and Google Scholar to capture preprints and a broader range of publications. The search time frame was restricted to October 2016 to August 2025 to capture the most dynamic phase of AI advancement in materials science.

A composite keyword strategy was employed, combining terms related to three aspects: (1) AI Techniques (e.g., “artificial intelligence”, “machine learning”, “deep learning”, “neural network”); (2) Biomaterial Categories (e.g., “biomaterial*”, “scaffold”, “hydrogel”, “implant”, “polymer”, “metal”, “ceramic”, “composite”); (3) Research Tasks (e.g., “design”, “property prediction”, “optimization”, “synthesis”, “high-throughput screening”). This search yielded an initial pool of over 810 records.

3.2. Inclusion and Exclusion Criteria

To ensure the relevance and quality of the analysis, the following criteria were applied:

Inclusion Criteria:

Studies were included if they met all of the following: (1) were primary research articles; (2) explicitly applied ML/DL models to the design, prediction, optimization, or characterization of biomaterials for biomedical applications; (3) provided a clear description of the AI model development and validation process.

Exclusion Criteria:

Articles were excluded if they were: (1) reviews, editorials, book chapters, or conference proceedings; (2) studies where AI was used only for auxiliary data processing without core material design logic; (3) non-english publications; (4) articles with insufficient methodological detail for evaluation.

3.3. Study Selection

The study selection process followed a structured multistage screening protocol, summarized in Figure . After duplicate removal, the titles and abstracts of all retrieved records were screened independently by two authors against the inclusion/exclusion criteria. Potentially relevant articles then underwent a full-text review by the same two authors to determine final eligibility. Any disagreements during screening were resolved through discussion or consultation with a third author. From the initial pool of over 810 records, 127 core publications were ultimately selected for in-depth analysis and form the foundational corpus of this review.

1.

Visual representation of database search carried out in this article.

3.4. Data Extraction and Classification Methodology

The quantitative statistics and prevalence rates reported in this review are based on a systematic analysis of relevant literature published within the last five years. After retrieval and screening, a total of 127 studies were included for full-text review. A standardized data-extraction form was used to record key information (e.g., validation strategy, performance metrics, code/data-sharing statements). Classification decisions (e.g., whether a study performed external validation) were made based on explicit descriptions in the “Methods” or “Results” sections. To ensure consistency, a second reviewer independently extracted and classified a randomly selected subset (30%) of the papers. Inter-rater agreement (Cohen’s κ) was 0.86, and any discrepancies were resolved through discussion or arbitration by a third author. Borderline cases were handled conservatively and documented in the extraction log. A summary of key trends and bottlenecks in biomaterials AI research is provided in Table .

1. Synthesis of Key Trends and Bottlenecks in Biomaterials AI Research.

dimension & specific aspect	current status & typical characteristics	core problems, bottlenecks, & consequences
Data Foundation: Type and Scale	Dominantly compositional and scalar property data; growing use of structural data (images, graphs). Data sets are predominantly small-scale (tens to hundreds of samples). ,	Scarcity and lack of standardization cause overfitting and weak generalization. Heterogeneous data impedes learning of deep structure–property relationships and data integration. ,
Research Objectives: Properties and Metrics	Mainly predicting mechanical/physicochemical properties; biological response prediction is the frontier. Relies on standard metrics (R2, AUC) but primarily on internal validation. −	Lack of external validation (<10% of studies) and standardized reporting leads to overestimated performance and impedes cross-study comparison. ,
Technical Methodology: Models and Performance	Coexistence of traditional ML (RF, XGBoost) and deep learning (CNN, GNN, GAN). High performance is commonly reported on internal tests. −	High internal scores create a performance illusion with poor external generalizability. Absence of benchmark data sets prevents meaningful performance comparison.
Research Practice: Reproducibility and Validation	Very low data/code sharing (<15%). Validation is mostly simple hold-out/k-fold cross-validation on single data sets (>85%).	Triggers a reproducibility crisis. The lack of rigorous validation fails to assess robustness to out-of-distribution data, hindering clinical translation.

4. Overview of Artificial Intelligence

Artificial Intelligence is a comprehensive technical science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. − Its core goal is to enable machines to perform complex tasks that typically require human intelligence, such as learning, reasoning, planning, recognition, and understanding. The development of AI has evolved from symbolic reasoning and knowledge engineering to statistical learning, and now to the data-driven paradigm represented by deep learning, becoming a core driver of the new technological revolution and industrial transformation.

Machine learning, as the core implementation means of artificial intelligence, provides key technical pathways for achieving the above goals by automatically learning patterns and regularities from data. The types and common architectures of machine learning are illustrated in Figure .

2.

Types of machine learning and commonly used frameworks: (a) type, (b) common architecture.

4.1. Foundational Machine Learning Paradigms

Machine learning approaches are broadly categorized by their learning mechanism, each suited to distinct research objectives within biomaterials:

Supervised Learning trains models on labeled data sets where the desired output (e.g., a biocompatibility score, Young’s modulus) is known. It excels at predictive tasks such as property regression or material classification but is inherently dependent on the quantity and quality of curated experimental data.

Unsupervised Learning operates on unlabeled data to discover intrinsic patterns or groupings. It is invaluable for exploratory analysis, such as identifying novel subclasses of biomaterials from high-dimensional characterization data or reducing feature dimensions for visualization, thereby mitigating data annotation burdens.

Reinforcement Learning (RL) involves an agent learning optimal sequential decision-making policies through interaction with a dynamic environment. Its potential in biomaterials lies in optimizing multistep processes, such as the real-time control of bioprinting parameters or the adaptive design of synthesis pathways.

4.2. Key AI Model Architectures for Biomaterials

The effectiveness of AI is dictated by how well a model’s inherent architecture aligns with the structure of the data. Several specialized neural network architectures have become central to the field:. −

Convolutional Neural Networks (CNN) are the de facto standard for processing grid-like data. In biomaterials, CNNs automate the analysis of microstructural images from electron microscopy or micro-CT scans, enabling the quantitative, high-throughput assessment of porosity, surface topography, and even cell–material interactions. ,

Graph Neural Networks (GNN) are specifically designed for non-Euclidean, graph-structured data. This makes them uniquely powerful for modeling molecular structures, where atoms and bonds naturally form a graph. GNNs are adept at predicting molecular properties, protein-biomaterial interactions, and facilitating the rational design of novel polymers or bioactive peptides.

Transformer Models, built on the self-attention mechanism, excel at capturing long-range dependencies within sequential or tokenized data. They have revolutionized the analysis of biological sequences (e.g., protein or peptide sequences) and are pivotal in integrating multimodal data (e.g., combining spectral, textual, and imaging information) for a holistic performance prediction. −

4.3. A Comprehensive Synthesis of Methodologies, Data Challenges, and the Path toward Trustworthy Design in AI-Driven Biomaterials Research

The integration of Artificial Intelligence (AI) into biomaterials science is not merely an incremental improvement but a foundational shift toward a predictive, data-driven discipline. This transformation promises to accelerate the discovery of novel implants, optimize drug delivery systems, and engineer intelligent scaffolds for tissue regeneration. To realize this potential, a clear synthesis of three core pillars is essential: the effective mapping of AI methodologies to specific biomaterial data types, a candid assessment of the severe data-centric challenges constraining progress, and a defined pathway toward building trustworthy, reliable, and impactful AI systems.

4.3.1. Methodological Alignment: Choosing the Right AI Tool for the Data

The efficacy of AI in biomaterials is intrinsically tied to the nature of the data. A one-size-fits-all approach is ineffective; instead, the field has evolved a sophisticated mapping where specific AI architectures are uniquely suited to particular data modalities.

For compositional and formulation datathe precise ratios of elements in an alloy or monomers in a polymertree-based ensemble methods like Random Forest and Gradient Boosting (XGBoost) reign supreme. Their power lies in handling complex, nonlinear relationships between input features while providing critical interpretability through feature importance scores, directly guiding experimental synthesis.

When the problem involves molecular and atomic-scale structure, the graph-like nature of molecules calls for Graph Neural Networks (GNNs). GNNs natively operate on bonds and atoms, making them ideal for predicting protein-biomaterial interactions or designing new bioactive peptides. For microstructural imagery from electron microscopes or CT scans, Convolutional Neural Networks (CNNs) are indispensable. Pretrained models like ResNet enable rapid analysis of surface roughness or porosity, while Generative Adversarial Networks (GANs) can create novel, realistic microstructures for virtual testing.

Temporal and sequential data, such as the controlled release kinetics of a drug or the time-lapse degradation of a scaffold, are the domain of recurrent neural networks, particularly long short-term memory (LSTM) networks, which are designed to recognize long-term dependencies in sequence data. Finally, the most complex challengespredicting the in vivo performance of an implant from its design specsrequire multimodal and hybrid approaches. These models, using multitask or ensemble learning, fuse data from composition, processing, structure, and biological response to build a holistic understanding of material behavior. A summary of optimal AI methodologies for different biomaterials data types is provided in Table .

2. Optimal AI Methodologies for Biomaterials Data Types.

data type	exemplary form	optimal AI methods	rationale & key advantage
compositional	alloy formulas, polymer recipes −	random forest, XGBoost, SVR −	excellent with tabular data, provides interpretable feature importance for guiding synthesis. ,
molecular/structural	molecular graphs, crystal structures	graph neural networks (GNNs), Transformers −	GNNs capture relational data (bonds, neighbors); Transformers excel with sequential data like protein strings. −
image/microstructural	SEM/TEM images, micro-CT scans	CNNs (ResNet, VGG), U-Net, GANs	CNNs are supreme at spatial feature extraction; GANs generate new structures for design and data augmentation. −
temporal/process	drug release profiles, degradation curves	LSTM networks, reinforcement learning (RL)	LSTM models long-term dependencies in sequences; RL optimizes sequential decisions (e.g., in 3D printing). ,
multimodal	linked composition, process, structure, and outcome data	multitask learning, hybrid models	integrates diverse data sources to build generalizable models of complex material performance. ,

4.3.2. The Foundational Challenge: A Crisis of Data Quality, Quantity, and Standardization

The sophistication of AI algorithms stands in stark contrast to the often-primitive state of the data that fuels them. This disconnect represents the single greatest bottleneck in the field. The crisis is 3-fold: extreme data scarcity, a profound lack of standardization, and the consequent cultivation of “brittle” AI models.

Biomaterials research generates intrinsically small data sets. High-throughput experimentation is costly, and biological validationespecially in vivo studiesis time-consuming and ethically constrained. It is common for studies to train complex deep learning models on mere dozens or hundreds of data points, a practice that almost guarantees overfitting. Compounding this scarcity is a pervasive lack of data standardization. Materials are described inconsistently, experimental protocols vary between laboratories, and key metadata is omitted. This creates isolated data silos, making it impossible to aggregate information across studies into the large-scale data sets needed for robust AI. A fundamental challenge for AI models in biomaterials is “domain shift,” which extends far beyond simple changes in data distribution to represent inherent, multistage disconnects in the R&D pipeline. First, assay-to-application shift is prevalent: for instance, a degradation model trained on data from standard static immersion tests may fail completely to predict material behavior under dynamic blood flow shear stress or complex enzymatic environments, as the physicochemical context of training differs drastically from application. Second, the most critical in vitro to in vivo shift constitutes a major translational barrier: a material showing excellent biocompatibility in cell culture may induce chronic inflammation or fibrous encapsulation in an animal model due to unmodeled immune responses, protein corona formation, or dynamic metabolic clearance. Furthermore, cross-laboratory shift cannot be ignored: even when following identical protocols, variations in cell line passage, reagent lots, instrument calibration, and operator techniques between laboratories introduce systematic biases, rendering a high-performing model developed in one lab ineffective in another. Consequently, the evaluation of model generalizability must move beyond simple random splitting of a data set and adopt a graded, translation-oriented external validation framework: the first step involves validation on data from a collaborative lab using the same protocol; the second step involves stress-testing on data from related but distinct biological models (e.g., from a rat to a rabbit bone defect model); and finally, progression toward prospective small-scale in vivo experiments. Only by successfully navigating these progressive “domain gulfs” can an AI model possess genuine potential to guide clinical translation. An overview of the data and evaluation challenges is presented in Table .

3. Data & Evaluation Crisis in Biomaterials AI.

challenge dimension	current problematic norm	consequence & risk	required paradigm shift
data scarcity & silos	small, lab-specific data sets; no standardized sharing.	models are overfitted and nongeneralizable; wasted resources on redundant experimentation.	build a FAIR Data Commons: Community-wide effort to create open, standardized, and aggregated databases. ,
model validation	high performance reported only on internal test splits.	illusion of capability; models fail in practical, external applications.	mandate External Benchmarking: Require validation on independent, community-held benchmark data sets.
generalization assessment	assumed but rarely tested across domains/laboratories.	prevents reliable deployment and scalable discovery.	implement Systematic Testing: Define protocols for cross-data set, out-of-distribution, and domain-shift evaluation.
uncertainty ignorance	predictions are almost always presented as certain single values.	unacceptable for biomedical risk assessment; hinders decision-making.	integrate Uncertainty Quantification: Make Bayesian methods or ensemble uncertainty a standard model output. ,
interpretability gap	complex models treated as opaque oracles.	limits scientific insight, erodes trust with clinicians and regulators.	adopt Explainable AI (XAI): Use SHAP, LIME, etc., to explain predictions and validate against domain knowledge. −

The direct consequence is the proliferation of brittle models. These models may achieve spectacular accuracy (R ² > 0.95) on their internal, narrowly drawn test set but fail completely when presented with data from a different laboratory or a slightly different material composition. This failure of generalization renders them useless for real-world design and discovery. Furthermore, the field has largely neglected uncertainty quantification. In a domain impacting human health, a point prediction is inadequate; we must know the confidence interval around that prediction to assess risk. The absence of this practice, along with the “black-box” nature of complex models, severely erodes the trustworthiness required for clinical translation.

4.3.3. The Path Forward: Building a Trustworthy, Collaborative, and Impactful Ecosystem

To transition from demonstrating potential to delivering reliable impact, the biomaterials AI community must move beyond isolated proofs-of-concept. The path forward requires a coordinated, community-driven effort focused on building trustworthy systems through infrastructure, standards, and deep collaboration.

The first and most urgent step is the construction of a robust data ecosystem. This involves establishing public data repositories governed by the FAIR (findable, accessible, interoperable, reusable) principles. Journals and funding agencies must mandate data deposition in standardized formats with rich metadata. Concurrently, the community must define and adopt universal benchmarks. These benchmark data sets and challenges (e.g., “Predict the osteointegration potential of a coated implant from its design parameters”) will provide a level playing field to compare algorithms fairly and drive progress on generalization, not just accuracy. −

Technologically, the next generation of models must be inherently more trustworthy. This means the routine incorporation of uncertainty quantificationtreating it not as an optional add-on but as a fundamental model output. It also means advancing beyond purely data-driven black boxes by developing physics-informed neural networks (PINNs) and hybrid models that embed known physical laws (e.g., mass transport, mechanics) to improve extrapolation and interpretability. Furthermore, the use of Explainable AI (XAI) techniques to audit and interpret model decisions must become standard practice to build credibility with end-users. ,

Ultimately, these technical and infrastructural advances can only succeed within a renewed framework of interdisciplinary collaboration. The silos between material scientists, biologists, computer scientists, and clinicians must be dismantled. Shared problem definitionstarting with clinical needsis essential. The goal is to foster an integrated pipeline where AI-driven design proposals are rapidly prototyped, experimentally validated, and the resulting data is fed back to refine the models, creating a virtuous, accelerating cycle of innovation. By committing to this path, AI can mature from a promising analytical tool into the cornerstone of a new era of rational, efficient, and safe biomaterial design.

4.4. Data Foundations for AI in Biomaterials Research: Systemic Challenges and a Framework for Trustworthy Systems

The integration of Artificial Intelligence (AI) and Machine Learning (ML) is profoundly reshaping the paradigms of biomaterial design, characterization, and evaluation. As comprehensively reviewed by Gokcekuyu et al. AI methodologies have demonstrated remarkable efficiency advantages over traditional trial-and-error approaches, from predicting the mechanical properties of polymers and optimizing the phase composition of high-entropy alloys to designing osteoinductive ceramic scaffolds. However, both reviews astutely highlight that beneath this flourishing landscape lies a fundamental crisis in data and methodology. The majority of advancements are built upon fragile data foundations, casting serious doubt on the reliability, reproducibility, and clinical translatability of the models. This section synthesizes these insights to systematically dissect four core challenges and propose concrete pathways for constructing the next-generation, trustworthy framework for AI in biomaterials research.

4.4.1. Data Scarcity and Heterogeneity: From Isolated “Small Data” to an Interconnected “Intelligent Data Ecosystem”

Core Challenge: Current AI applications heavily rely on limited-scale, isolated, self-constructed data sets. Many pivotal studies utilize data sets containing only tens to hundreds of samples with limited feature dimensions. This “small data” reality clashes sharply with the data-hungry nature of deep learning, directly leading to model overfitting. Consequently, models memorize data set-specific noise rather than learning universal “composition-structure–property” relationships, resulting in sharply declining performance when applied to new material systems or biological environments. Furthermore, the high heterogeneity of dataencompassing multimodal information such as chemical composition, imaging, mechanical curves, and genomicsposes significant challenges for data fusion and standardization. ,

Integrated Recommendations and Implementation Path: Building upon the calls for data sharing emphasized in both reviews, a multipronged strategy is essential. First, the field must collaboratively establish FAIR-compliant domain-specific databases. This requires developing and enforcing mandatory metadata standards to ensure each data entry is accompanied by complete synthesis protocols, characterization parameters, and biological experimental conditions, transforming “data silos” into a “data network.” Second, the “pretraining and fine-tuning” paradigm should be widely adopted. Inspired by initiatives like the Polymer Genome project, large-scale general materials databases should be used to pretrain deep neural networks, embedding fundamental physicochemical principles. These pretrained models can then be efficiently fine-tuned with limited, task-specific biomedical annotations, enabling effective knowledge transfer and mitigating data scarcity. Finally, the field should advance physics-informed data augmentation techniques. Integrating computational methods like phase-field simulations and molecular dynamics can generate plausible virtual material data under strict thermodynamic and kinetic constraints. Exploring conditional generative adversarial networks guided by known biological response rules can also help synthesize data, enabling low-cost, efficient exploration of the vast materials design space.

4.4.2. Measurement Variability and Labeling Subjectivity: Toward a Revolution in Standardization and Objectivity

Core Challenge: The reproducibility of biomaterials data is plagued by two major factors. The first is technical variability: differences in instrument calibration, operational protocols, and environmental controls across laboratories introduce systematic biases in measuring key properties, making cross-study data integration akin to comparing “apples and oranges.” The second, and more damaging, is semantic variability: the evaluation of complex biological end points like “biocompatibility” or “osteoinductivity” heavily relies on researchers’ subjective judgment and localized, nonstandardized experimental protocols, lacking globally accepted quantitative “gold standards.” Label noise and subjectivity are particularly detrimental in biomaterials evaluation because many critical end points are inherently qualitative and expert-dependent. For example, the scoring of “inflammation response grade” or “quality of new bone formation” in histological sections heavily relies on the subjective experience and judgment criteria of pathologists, exhibiting significant inter-rater and intrarater variability. This inherent fuzziness of the “gold standard” directly contaminates the target signal in supervised learning. Similarly, reducing complex biological responses (e.g., “osteogenic differentiation”) to binary labels (yes/no) based on a single staining threshold (e.g., Alizarin Red staining) not only discards rich continuum information but also makes the labels highly sensitive to experimental conditions (e.g., staining time, pH). To address this challenge, we advocate a dual strategy: First, promote the standardization and quantification of the label generation process. This can be achieved, for instance, through online collaborative platforms where multiple experts perform blinded back-to-back assessments, ultimately generating probabilistic labels (e.g., “this sample was rated “moderate inflammation” by two out of three experts, confidence 66%”) to replace arbitrary binary classifications. Second, develop end-to-end multimodal learning models. Such models can bypass noisy, human-defined intermediate labels and directly learn the mapping from raw high-dimensional data (e.g., chemical descriptors of the material, micro-CT images, or even transcriptomic sequencing data) to final clinically relevant outcomes (e.g., in vivo bone volume fraction, healing time). This not only reduces dependence on subjective labels but also holds promise for discovering novel biomaterial design principles beyond traditional evaluation systems.

This inconsistency in labeling severely corrupts the supervisory signal in machine learning. Integrated Recommendations and Implementation Path: A concerted effort toward standardization is imperative. To address measurement variability, cross-laboratory initiatives must be launched to develop and disseminate detailed standard operating procedures for key performance indicators. Concurrently, establishing and distributing certified reference biomaterials will serve as benchmarks for interlaboratory data comparison and normalization, fundamentally reducing measurement noise. To combat labeling subjectivity, the field must develop expert consensus-driven intelligent annotation platforms. For complex histology images or cell behavior data, online collaborative platforms integrated with active learning algorithms can identify samples with high annotation disagreement, triggering multiple rounds of blind expert annotation and discussion. The outcome should be probabilistic labels that reflect consensus confidence, replacing arbitrary binary classifications and enhancing label objectivity and informativeness. , Furthermore, encouraging end-to-end multimodal learning can reduce reliance on variable, single intermediary metrics. Developing multimodal fusion networks capable of processing raw high-dimensional datasuch as chemical composition, micro-CT images, and transcriptomic datato directly predict in vivo outcomes like bone volume or immune response can uncover new design principles beyond traditional evaluation systems.

4.4.3. Data Splitting Bias and Evaluation Distortion: Establishing a Rigorous Model Validation Regime

Core Challenge: In biomaterials research, many flaws in model evaluation stem from ignoring the inherent clustered structure of data generation, leading to severe data leakage. The most critical forms of leakage are not random but systematic: Interbatch leakage: When material samples from the same synthesis batch (sharing identical raw material purity, process fluctuations, and environmental conditions) are randomly assigned to both training and test sets, the model can easily learn the specific “fingerprint” of that batch (e.g., characteristic impurity profiles or microstructural flaws) rather than the universal “composition-structure–property” relationship. The performance of such a model plummets when confronted with a new synthesis batch that inevitably contains variations, rendering it useless for guiding process scale-up and mass production. Biological donor leakage: In studies based on primary cells or animal experiments, if multiple data samples from the same donor or the same animal appear in both training and test sets, the model captures the unique biological background noise of that individual (e.g., specific immune status or metabolic baseline). This prevents the model from predicting the universal response of the material across a heterogeneous population, completely undermining its reliability for preclinical prediction. Temporal/Process leakage: When optimizing fabrication processes like 3D printing or electrospinning, sequentially collected data points exhibit temporal autocorrelation (reflecting equipment drift, environmental fluctuations). Randomly splitting this data allows the model to “memorize” temporal trends rather than the true process-parameter-to-performance mapping. Therefore, we strongly advocate for and hereby establish a methodological standard: the “group-based data splitting” strategy must be employed. Splitting should be performed at the level of nonindependent experimental units, ensuring that all data from an entire material synthesis batch, an entire animal individual, or an entire independent experimental process run is placed wholly into either the training, validation, or test set. Journals should require authors to explicitly report their splitting rationale. Furthermore, model validation should encompass three tiers: rigorous internal cross-validation, external validation (I) on data from a collaborative lab using the same protocol, and a heterogeneous stress test (II) on data from different material systems or biological models. Only models that pass this final tier can be considered to possess the necessary robustness and generalizability.

Integrated Recommendations and Implementation Path: Ensuring methodological rigor requires mandatory adherence to domain-informed data splitting strategies. All research must employ group-based splitting strategies, guaranteeing complete separation of training, validation, and test sets based on critical confounding factors like material batch, animal source, or experimental timeline. Academic journals should require authors to explicitly report and justify their data splitting methodology. Building on the need for robust validation highlighted in the reviews, a three-tiered model benchmarking system should be established: Internal Validation using rigorous cross-validation; External Validation I (Homologous) testing models on independently generated data from collaborative laboratories under similar conditions; and External Validation II (Heterogeneous Stress Test) challenging models with data from entirely different material systems or biological models. Only models passing this ultimate test can be considered to possess reliable generalizability. Additionally, systematic evaluation of model robustness and fairness is necessary, employing adversarial testing to simulate input perturbations and auditing performance across different data subgroups to ensure stability and equity. −

4.4.4. Lack of Uncertainty Quantification and Explainability: From Black-Box Prediction to Transparent Decision Support

Core Challenge: Most current AI models operate as “black boxes,” providing only point-estimate predictions without measures of confidence or explanations for their reasoning. In biomedical applications, a prediction without an associated uncertainty is nearly useless and potentially dangerous. Furthermore, model opacity hinders researchers from understanding the underlying mechanisms of AI discoveries, impeding the translation of data-driven correlations into testable scientific hypotheses.

Integrated Recommendations and Implementation Path: The field must transition toward probabilistic deep learning as a standard. Encouraging the adoption of models like Bayesian Neural Networks, Deep Ensembles, and Gaussian Process Regression, which naturally provide predictive distributions, is crucial. Reporting uncertainty metricssuch as 95% prediction intervals and calibration errorsalongside traditional accuracy scores should become mandatory. To bridge the explainability gap, methods like SHAP and LIME should be systematically integrated not just for posthoc local explanations but for global analysis of the key features driving model decisions. This helps identify previously unknown material descriptors and verifies whether models rely on biologically intuitive features, thereby building researcher trust. Most innovatively, model uncertainty should be leveraged as a valuable resource to guide research. In a closed-loop system, AI can not only recommend top-performing candidates but also identify regions of the design space with the highest predictive uncertainty. Prioritizing experimental exploration in these “cognitive blind spots” allows for optimal resource allocation to expand knowledge boundaries, paving the way for AI-driven autonomous scientific discovery.

4.5. A Multi-Level XAI Framework for Biomaterials: From Technical Validation to Mechanistic Discovery

In the field of biomaterials, which places a high premium on mechanism and safety, the “black-box” nature of advanced AI models (especially deep learning) constitutes a core bottleneck for their scientific adoption and clinical translation. Explainability here is not a nice-to-have feature but a rigid requirement for building trust, facilitating discovery, and meeting regulatory standards. First, at the scientific discovery level: a model that can only predict performance without explaining its rationale is like an answer without a derivation process; it cannot generate testable new scientific hypotheses (e.g., is it surface topography or chemical functional groups that dictate cell fate?). Second, at the translational and regulatory level: global regulatory agencies (e.g., FDA, NMPA, EMA) require a mechanism-based rationale for the safety and effectiveness of medical devices during review. A biomaterial designed by AI with an opaque decision logic will face fundamental skepticism during submission. Therefore, developing and systematically applying Explainable AI (XAI) techniques is the key bridge for transforming data-driven correlations into causal insights, defensible design decisions, and compliance-ready evidence. This section explores how to build a multilevel XAI framework for biomaterials. Interpretable AI (XAI) frameworks are being actively developed and applied within biomaterials research to bridge the gap between high-performance predictions and scientific understanding. These frameworks operate across multiple levels. At the feature level, techniques like SHAP (SHapley Additive exPlanations) analysis are employed to quantify the contribution of input parameters. For instance, in a study on magnesium alloy design, SHAP analysis revealed a nonlinear effect of zinc content (within 4–6 wt %) on corrosion resistance, attributing positive SHAP values to its role in optimizing protective layer formation. At the sample level, methods such as LIME (Local Interpretable Model-agnostic Explanations) are adapted with domain-specific metrics to explain individual predictions. At the model level, advanced concepts like material-science-informed concept activation vectors (CAVs) can be constructed to map learned representations to human-understandable concepts (e.g., surface energy, specific structural motifs). The evaluation of such XAI outputs in the literature increasingly focuses on quantitative metrics for faithfulness (e.g., how accurately the explanation reflects the model’s reasoning) and robustness (e.g., consistency across different explanation methods). Furthermore, the integration of interpretability constraints into inverse design pipelines has been shown in specific cases to improve the success rate and experimental efficiency of proposed material designs. These approaches collectively move the field toward more reliable, interpretable, and actionable AI-assisted discovery. Interpretable AI in Biomaterials Research: Practices, Limitations, and the Potential for Scientific Discovery.

While the predictive power of artificial intelligence (AI) in biomaterials has been amply demonstrated, its inherent nature as a “black box” remains a fundamental bottleneck to its scientific credibility and clinical trust. Current research on Explainable AI (XAI) predominantly remains at the level of acknowledging its importance or perfunctorily applying popular tools like SHAP and LIME to “satisfy” the need for interpretability. There is a critical lack of rigorous evaluation regarding whether and how these tools can uncover genuine physicochemical mechanisms. To elevate XAI from a posthoc, auxiliary visualization technique to an engine for scientific discovery, we must deeply understand its methodological core, its successful paradigms, and its fundamental limitations.

4.5.1. Feature Attribution Methods: Uncovering Key Descriptors and the “Correlation ≠ Causation” Trap

Feature attribution methods aim to quantify the contribution of each input feature (e.g., elemental concentration, processing parameter, molecular descriptor) to an individual prediction or the overall model behavior. Among these, SHAP (SHapley Additive exPlanations), due to its solid game-theoretic foundation and unified framework for global and local explanations, has become the most prevalent tool in biomaterials research. Successful Practices and Scientific Insights: In successful applications, SHAP does more than list important features; it validates or discovers new physicochemical principles through dialogue with domain knowledge. For instance, in studies predicting the formation of single-phase solid solutions in high-entropy alloys (HEAs), SHAP analysis not only confirms the established critical roles of atomic size difference and electronegativity but can also reveal nonlinear, combinatorial effects of specific elemental pairs on phase stability. This insight directly guides subsequent alloy composition design. As demonstrated in research on drug release from polymer matrices, SHAP analysis can elucidate how a few core descriptors a few core descriptorssuch as molecular weight and the ratio of hydrophilic to hydrophobic segmentsgovern release kinetics. This allows researchers to focus complex formulation optimization on the most critical control variables.Core Limitations and Failure Risks: However, feature attribution is fraught with hermeneutical pitfalls/Correlation vs Causation: SHAP reveals the statistical associations relied upon by the model, not necessarily the underlying biological or physical causal mechanisms. For example, a model predicting osseointegration might highly depend on the feature “calcium ion release rate.” SHAP would assign it high importance. This does not mean releasing calcium ions is the sole or direct cause of bone formation; it may be highly collinear with a host of other variables not included in the model, such as surface hydroxylation degree or protein adsorption conformation. Blindly equating high-SHAP-value features with therapeutic targets can lead research astray. Sensitivity to Feature Engineering and Collinearity: The input features themselves are products of human design. If the inputs are highly correlated or complexly transformed features (e.g., converting raw elemental concentrations into various empirical parameters), the distribution of SHAP values becomes blurred and difficult to interpret. The model might achieve its predictions through any combination of collinear features, leading to unstable and nonunique attributions with ambiguous physical meaning. Inability to Explain Complex Interactions and Emergent Behaviors: For the deep, nonlinear feature interactions learned by deep neural networks, additive attribution methods like SHAP can only provide approximate decompositions. When a material’s property arises from the complex coupling of multiple factors at the microscale (e.g., synergy between stress fields and chemical microenvironments), simple feature importance rankings may completely fail to capture the essence of such emergent behavior.

4.5.2. Counterfactual Analysis and Hypothesis Generation: From “What Is” to “What If”

Counterfactual analysis moves beyond describing “what the model sees” to exploring “how to change the input to achieve a desired output.” It generates counterfactual samples to answer questions like: To conceptually illustrate, one could ask what minimal changes to this bioceramic’s properties (e.g., increasing porosity from 40% to 60% while decreasing grain size from 5 to 2 μm) would change its osteoinductivity prediction in a hypothetical model from “low” to “high”. Successful Practices and Scientific Insights: Ideally, counterfactual analysis is a powerful bridge connecting AI prediction to experimental design. For instance, for a model described in a study predicting the degradation time of polymer scaffolds, counterfactual analysis could generate virtual molecules with minimal modifications to test hypotheses. This directly generates testable hypotheses for rational molecular modification to extend degradation time, greatly accelerating material design. Core Limitations and Failure Risks: The utility of counterfactual analysis is severely constrained by the “physical plausibility” of the generated samples: Lack of Feasibility Constraints: Most general counterfactual generation algorithms are ignorant of materials science constraints. They might suggest increasing the oxygen content in a titanium alloy to 25% for better corrosion resistancea phase that is thermodynamically impossible. Without counterfactual generation that incorporates domain knowledge like phase diagrams, reaction kinetics, and synthetic pathways, the output is a plethora of meaningless “fantasy” materials.

Locality and Single Solutions: Counterfactual methods typically find only one possible path from the starting point to the target. In materials design, however, there are often multiple equivalent or near-equivalent paths (i.e., different composition/process combinations that yield similar properties). Existing methods struggle to map the entire high-performance “design contour.”

Dependence on Potentially Distorted Decision Boundaries: The generation of counterfactuals is heavily reliant on the model’s own learnedand potentially inaccuratedecision boundaries. If a model incorrectly believes “all high-silica glasses are bioinert,” then counterfactual analysis will never suggest exploring certain high-silica compositions that might, in reality, be bioactive. This reinforces the model’s bias and stifles discovery.

4.5.3. Toward Mechanistic Explanation: A Path Integrating XAI with Domain Knowledge

To achieve genuine scientific explanation in biomaterials research, we must move beyond the superficial application of generic XAI tools and develop a mechanistic explanation framework deeply integrated with domain knowledge. Constructing an “Interpretability Validation Loop”: the output of XAI should not be the end of analysis but the start of new experiments or simulations. This suggests a validation loop where, for example, if SHAP analysis in a prior study identifies a “specific fractal dimension” as critical, subsequent independent experiments could fabricate surfaces to test this causality. This closes the loop from “data correlation” to “mechanism confirmation.” Developing Domain-Informed XAI Models: Build interpretability into the model from the outset. Strategies to enhance interpretability include using physically meaningful descriptors as inputs, or constructing Physics-Informed Neural Networks (PINNs) that embed governing equations that embed governing equations as soft constraints in the loss function. This makes the model’s learning process more aligned with physical reality, and its parameters and intermediate representations are more readily interpretable as meaningful physical quantities (e.g., diffusion coefficients, interfacial energy). Employing Multi-Scale, Multi-Tool Cross-Validation: Do not rely on a single XAI tool. Combine feature attribution (to understand key inputs), techniques like Activation Maximization or Concept Bottleneck Models (to understand what abstract concepts are learned in intermediate network layers), and instance-based methods (to find prototype samples). Explanations gain credibility only when insights from different methods corroborate each other and align with domain knowledge. Embracing the Concept of “Levels of Explanation”: For extremely complex systems (e.g., material-immune system interactions), seeking a completely transparent, clear causal chain may be unrealistic. A more pragmatic strategy is to establish different levels of explanation: the statistical association level (which features are correlated), the interventional level (what changes produce what outcomes), and the mechanistic level (what underlying biomolecular pathways are involved). Each level has its value and guides experimental validation of corresponding precision.

4.6. The Open Science Paradox in Biomaterials AI: Navigating the Landscape from Proprietary Black Boxes to an Incentive-Aligned Ecosystem

The intersection of artificial intelligence and biomaterials research presents a complex and paradoxical landscape regarding open data and code practices, characterized by a profound disconnect between awareness and implementation. While authoritative reviews have systematically identified poor data quality, opaque workflows, and lack of reproducibility as fundamental bottlenecks hindering the transition from proof-of-concept demonstrations to reliable scientific discoveryand have explicitly called for a new research paradigm built upon open databases, containerized workflows, and mandatory code sharinga critical examination of published literature reveals that the vast majority of studies remain trapped in a “high-performance black box” dilemma. These works heavily rely on “proprietary data sets” that are privately curated and never disclosed. The entire data generation chainfrom raw material synthesis parameters and processing conditions to multidimensional characterization signals and final performance labelslacks standardized documentation and is devoid of public access, rendering independent verification impossible. Concurrently, methodological descriptions are excessively oversimplified. Common phrases in publications, such as “a random forest model was used for prediction” or “convolutional neural networks were employed for image analysis,” completely obscure crucial technical details. These include the specific logic for partitioning training, validation, and test sets (including whether temporal or batch-related data leakage was avoided), the process for hyperparameter tuning, steps for feature engineering, and the random seeds used across multiple experiments. This omission of information makes the research process akin to a building without architectural blueprints; replication based solely on a photograph of the facade is infeasible. More critically, model validation is predominantly based on “self-verification,” reporting high accuracy metrics only on internal data from the same laboratory and batch, while entirely avoiding stress tests on external independent data sets, materials produced via different processes, or complex biological environments closer to real-world applications. Consequently, the robustness and generalizability of the results are significantly undermined, rendering many purported “breakthroughs” effectively nontransferable and noncumulative contributions to the scholarly corpus.

Against this backdrop, the development of open resources in the field resembles “sparse oases amidst a vast desert.” Exemplary open resources are concentrated primarily in three tiers: First, pioneering projects like the Polymer Genome, which genuinely strive to construct open-source knowledge ecosystems integrating high-throughput computational data, experimental validation, and machine learning models. Second, long-established public repositories such as the Protein Data Bank (PDB), Inorganic Crystal Structure Database (ICSD), and the Materials Project, which provide benchmark data for predicting certain material properties. Third, widely adopted open-source algorithm libraries and explainability tools like Scikit-learn, PyTorch, and SHAP, which establish a foundation for methodological transparency. However, the area illuminated by these open resources is far eclipsed by the extensive shadow region still shrouded by proprietary barriers. The composition of this closed domain is complex and entrenched: First, vast quantities of data and discoveries generated through industry-academia-research collaborations, bound by strict confidentiality agreements and intellectual property clauses, creating a dynamic where “the frontier lies in industry, but knowledge remains in a vault.” Second, “high-value mappings” directly linked to productization and market competitiveness, such as AI-optimized chemical formulas for biocoatings, quantitative relationships between the surface micronano structure of metallic implants and their in vivo osteogenic efficacy, or precise processing windows for drug carriers achieving targeted release. These constitute core corporate assets, and their confidentiality holds commercial rationale. Third, raw, multidimensional data streams generated by sophisticated instrumentation (e.g., ultrahigh-resolution in situ observation platforms, high-throughput synthesis robots), whose acquisition, storage, and analysis heavily depend on customized software and hardware, creating technical barriers that inherently impede sharing. Most ubiquitous yet often overlooked are the ″disposable″ data processing scripts, custom model architecture files, and trained weight parameters that proliferate across global laboratoriesgenerated alongside each paper but rapidly rendered obsolete after publication. This microlevel “knowledge evaporation” incurs immeasurable costs in duplicated effort and lost innovation potential.

Confronted with this deep-seated tension between openness and secrecy, simplistic moral advocacy is ineffective. It is essential to acknowledge that the root cause lies in the fundamental conflict between the public nature of scientific inquiry and the demand for verification, versus the private attributes of technological innovation and the imperative for benefit protection. Therefore, constructing an effective open science ecosystem cannot aim for a one-size-fits-all approach to full transparency. Instead, it requires designing a nuanced, tiered, and incentive-compatible implementation framework. At the foundational level, there should be a mandatory requirement to publish all computational code for data processing and analysis, along with workflow environment configuration files (e.g., Dockerfile). This ensures the logical process from raw data to published figures is reviewable and reproducible, upholding the baseline of scientific verifiability. At the data level, a “graded openness” strategy should be promoted. Immediate openness is advocated for process data not involving core secrets (e.g., intermediate characterization spectra of materials, nonoptimal process parameter sets). For sensitive core formulations or performance data, mechanisms such as “fingerprint summary” disclosure (e.g., publishing characteristic spectral signatures rather than complete compositions) or delayed openness after a set protection period (e.g., postpatent grant) can be explored. At the collaboration level, initiatives to establish controlled-access platforms, akin to a “Biomaterials Data Consortium,” could be advanced. Members, adhering to a common agreement, could share and query nonpublic data within defined scopes, fostering limited, trust-based collaboration at the frontier.

Achieving this systemic transformation necessitates concerted action from multiple stakeholders: The academic community must lead in developing an International Standard for Biomaterials Data and Metadata Management, defining minimum information sets required for recording various data types. Leading academic journals should collaborate to reform submission and review policies, making data and code availability, along with reproducibility, a prerequisite for publication, and introducing “reproducibility review” stages. Research funding agencies and academic evaluation systems must fundamentally reorient their value assessments, recognizing the construction of high-quality data sets, sustained maintenance of open-source software, and contributions of reproducible research packages as academic achievements on par with, or even exceeding, traditional paper publications, and establishing dedicated awards. Finally, at the stage of talent cultivation, essential open science practicessuch as data management, version control, and reproducible computingmust be integrated as compulsory skills into the curricula of materials science and bioengineering programs, nurturing a new generation of researchers ingrained with the ethos of open science. Only through such multilayered, systematic reform can open science evolve from a conceptual advocacy by a few pioneers into a robust infrastructure and cultural norm, capable of driving the entire biomaterials field toward credible, efficient, and cumulative innovation.

5. AI-Assisted Biomaterials Preparation

The rise of artificial intelligence technology is ushering the field of biomaterials preparation into a new intelligent era, with its core lying in the systematic transformation of traditional R&D paradigms through data-driven methods. Specifically, AI deeply empowers the biomaterials development process from the following three key dimensions: accurately predict material properties from a large amount of experimental data.; proactively constructing new material structures with target properties through inverse design and generative models; rapid multiobjective optimization and synthesis control, encompassing compositional tuning, and intelligent adjustment of process parameters. − These four aspects are interlinked, collectively forming an intelligent R&D system that integrates the entire chain from “design-prediction-preparation,” significantly accelerating the innovation and application of functionalized, personalized biomaterials.

5.1. Precision Prediction

Artificial intelligence enables precision prediction in biomaterials by establishing composition-structure–property relationships. High-throughput screening exemplifies this approach, combining automated experimentation with machine learning to efficiently evaluate material candidates. This method not only predicts key properties like biocompatibility and mechanical performance, but also guides experimental design through active learning, significantly accelerating the discovery of high-performance biomaterials. High-Throughput Screening (HTS) is an experimental method that uses automation technology to rapidly synthesize and characterize large libraries of material samples in parallel, thereby accelerating the discovery of materials with target properties. − In the biomaterials field, researchers construct libraries containing thousands of samples with different compositions, ratios, or process conditions in microplates and automatically test key indicators such as biocompatibility, mechanical properties, or drug release behavior, enabling large-scale experimental screening.

However, traditional HTS has significant limitations. Its main problem is that experimental design often relies on predefined, limited parameter ranges, essentially remaining a high-cost “grid search” with considerable blindness. Simultaneously, the massive amounts of data produced by experiments are often used only to screen the best samples in the current batch, failing to fully exploit the complex “composition-structure–property” relationships hidden behind the data. This results in a situation of “data-rich but knowledge-poor,” consuming substantial resources on experiments with low information density.

AI empowerment upgrades HTS into a closed-loop intelligent system. First, through algorithms like active learning, AI can intelligently recommend the first batch of samples with the highest information content from the vast materials design space, significantly improving the efficiency of information gain from experiments. − In the data analysis stage, computer vision and machine learning models can automatically process complex characterization data (e.g., cell microscopy images, spectra, or mechanical curves), enabling high-throughput, quantitative performance evaluation. Subsequently, AI uses this data to train performance prediction models, which can not only accurately predict the properties of new formulations but also inversely recommend the optimal material composition and process parameters that meet specific application requirements, guiding the targeted design of the next round of experiments. The integration of AI and HTS brings fundamental changes. Its core advantage is the ability to efficiently process massive data, identify complex nonlinear relationships, and quickly pinpoint optimal candidate materials or compounds, drastically shortening the R&D cycle and reducing experimental costs. It enables deep knowledge mining from material data and can break through the limitations of human experience to discover novel, high-performance biomaterial formulations in a broader design space, thereby comprehensively accelerating the innovation and clinical application process of biomaterials. ,

In Figure a, Liu successfully established a generalizable AI-assisted biomaterials design process by combining 3D printing, high-throughput experimentation, and machine learning. Using an XG Boost model to fit nonlinear relationships, and enhancing model performance through data augmentation and feature engineering, the process systematically revealed the complex relationship between structural parameters of CaP ceramics and their osteoinductive performance and identified the optimal range of structural parameters. In the study illustrated in Figure b, Zhao proposed a machine learning (ML)-assisted high-throughput screening strategy for rapidly discovering efficient nanozymes for treating ulcerative colitis (UC). The study involved constructing a database of 4104 AB₂X₄ structure materials, combined with density functional theory (DFT) calculations and various ML models (e.g., XGBoost, RF), to predict key performance indicators such as antioxidant activity, acid stability, and Zeta potential. Innovatively employing the SISSO (Sure Independence Screening and Sparsifying Operator) symbolic regression method revealed semiquantitative structure–activity relationships between material features and enzymatic activity, significantly improving the precision and efficiency of nanozyme design. The finally screened SrDy₂O₄ nanozyme exhibited excellent therapeutic effects and CT imaging capabilities both in vitro and in vivo. This study not only advanced the application of AI in biomaterials design but also provided an interpretable, data-driven intelligent screening paradigm for complex disease treatment.

3.

AI-Related Applications in HTS: (a) Schematic diagram of DLP-based 3D printed scaffold derived from a novel high-throughput screening plus machine learning approach, Reproduced or adapted with permission from ref Copyright 2025 Elsevier (b) schematic diagram of a rational design process for multifeatured nanozymes in UC therapeutics, reproduced or adapted with permission from ref Copyright 2025 John Wiley and Sons (c) Schematic diagram of machine-learning assisted approach for Ti alloy discovery, Reproduced or adapted with permission from ref Copyright 2020 Elsevier.

In Figure c, Wu’s research developed an artificial neural network (ANN) model named “βLow” for efficiently predicting the Young’s modulus and martensite start temperature (Ms) of β-titanium alloys, thereby recommending new titanium alloy compositions with low modulus (<50 GPa) and low cost. Using AI trained on only a small sample of experimental data (164 modulus data points and 112 Ms temperature data points), the model achieved high prediction accuracy and successfully recommended the Ti–12Nb–12Zr–12Sn alloy, which is difficult to discover by traditional methods. This alloy possesses a bone-like modulus (42 GPa), high strength, and good biocompatibility. βLow broke through the efficiency bottleneck of traditional alloy design, demonstrating the powerful capability of machine learning in high-throughput screening and low-cost optimization of materials, providing an important example of the practical application of AI in materials science. Zhu et al. developed an AI-enhanced, droplet-based high-throughput screening strategy that significantly advances the optimization of cell-free gene expression (CFE) systems. By combining microfluidics-generated picoliter droplets with fluorescent barcoding, the platform enables efficient and large-scale combinatorial screening of reaction conditions with minimal reagent use. Machine learning models, including neural networks and XGBoost, are trained on droplet data to predict optimal formulations beyond experimental sampling. Applied to an Escherichia coli-based CFE system, DropAI identified three essential components and optimized their concentrations, reducing unit cost by 4-fold and increasing yield 1.9-fold for sfGFP while maintaining performance across diverse proteins. Furthermore, via transfer learning, the optimized E. coli model was adapted to a Bacillus subtilis system, doubling its yield with limited new data.

5.2. Inverse Design

Inverse Design is a target-driven materials R&D paradigm. Its core logic is completely opposite to the traditional “trial-and-error” method: it does not start from known materials to test their properties, but first defines the target properties (e.g., degradation at a specific pH, a certain elastic modulus), and then uses computational methods to inversely deduce the ideal material composition or structure that can achieve those properties. This approach transforms materials development from “blind search” to “directed solving”. , Traditional inverse design often heavily relies on researchers’ experiential knowledge and simple theoretical models. Researchers need to manually establish mapping relationships between material composition, structure, and properties based on physicochemical rules, and perform iterations through calculations (e.g., based on thermodynamic rules or coarse-grained simulations). This method is computationally inefficient and struggles to handle the multivariable, strongly nonlinear complex systems of biomaterials, often only applicable to highly simplified models or limited design spaces. It is crucial to note that inverse design of biomaterials is by no means a simple single-objective search but is fundamentally a highly constrained multiobjective optimization problem. An ideal material must find the optimal balance between competing, often conflicting, performance metrics. For instance, the design of a scaffold for bone regeneration must navigate the trade-offs within the “impossible triangle” of high porosity (promoting cell migration and nutrient diffusion), sufficient mechanical strength (providing initial mechanical support), and controlled degradation rate (matching new bone growth). Similarly, complex trade-offs exist between high drug loading, low burst release, good stability, and specific targeting ability when designing targeted delivery vehicles. Therefore, the core value of AI-driven inverse design lies not only in rapidly finding individual candidate formulations but also in its ability to efficiently explore and map the “Pareto front” within the entire high-dimensional design space. This provides researchers with a clear tradeoff landscape, enabling data-driven, informed decisions based on specific clinical priority needs (e.g., prioritizing targeting efficacy in cancer therapy versus prioritizing sustained release in chronic disease management).

AI empowerment has completely changed the implementation path of inverse design. In the biomaterials field, AI-driven inverse design is typically based on trained performance prediction models: first, using large amounts of “composition-structure–property” data to train machine learning models (e.g., neural networks), enabling them to learn the complex mapping relationships between biomaterial composition and their target properties. Subsequently, optimization algorithms (e.g., Bayesian optimization, genetic algorithms) or generative models (e.g., Generative Adversarial Networks GAN, Variational Autoencoders VAE) are used, with the target properties as input, to inversely search for or directly generate material formulations or molecular structures that meet the requirements. By exploring high-dimensional parameter spaces, AI can quickly recommend innovative material design solutions beyond human experts’ intuition. ,

AI’s assistive role in this process is mainly manifested in two aspects: first, acting as a “super predictor,” accurately quantifying the complex nonlinear relationships between material formulations and properties; second, acting as a “generation engine,” proactively proposing a large number of candidate materials with target characteristics, greatly shortening the cycle from concept to design. Its core advantages are: First, AI can handle massive variables and complex constraints, enabling global optimal solution search and avoiding local optima; Second, it significantly reduces reliance on prior knowledge and can even discover novel biomaterial formulations that transcend human intuition; finally, when combined with high-throughput computation and automated experimentation, this method can form a closed-loop system of “design-prediction-validation,” comprehensively accelerating the R&D process of functionalized biomaterials (e.g., targeted drug carriers, tissue engineering scaffolds). ,

AI not only accelerates the discovery of high-performance materials but also provides theoretical insights into key design principles. In Figure a, Lu proposed a machine learning (ML)-based inverse design strategy for optimizing the structure and output signal performance of flexible tactile sensors. Its core innovation lies in introducing Support Vector Machines (SVM) and statistical learning criteria (e.g., classification accuracy, interclass separability and intraclass dispersion after t-SNE dimensionality reduction) into the sensor design stage, enabling data-driven optimization of fabrication parameters (e.g., electrode distribution, microstructure shape and density). This method significantly improved the classification accuracy (≈99.58%) for six types of dynamic touch modes and was successfully applied to handwriting recognition and real-time Braille decoding. Extending further to the design of biocompatible materials, in Figure b, Li proposed a deep learning-based inverse design framework (GPstack-RNN) for simultaneously optimizing material functionality and biocompatibility. Its AI core innovation integrates a generative model (G) and a predictive model (P): the generative model, based on a GRU network, learns molecular representations from SMILES strings and generates novel ionic liquid (ILs) structures; the predictive model uses a Recurrent Neural Network (RNN) to end-to-end predict their antibacterial activity and cytotoxicity without relying on traditional molecular descriptors. This framework enabled efficient, targeted screening of ILs with both high antibacterial activity and low toxicity in virtual chemical space, and their effectiveness was experimentally validated.

4.

AI Applications in Inverse Design: (a) Schematic diagram of the machine learning-assisted sensor design via fabrication parameters optimization, (b) Workflow of our proposed inverse design of biocompatible ILs., Reproduced from ref Copyright 2024 American Chemical Society (c) Schematic diagram of designing deployable implants using an AI inverse design approach, Reproduced from ref Copyright 2025 American Chemical Society (d) workflow of the proposed reverse engineering approach accompanied by data augmentation and ANN structure.

In Figure c, Jiao proposed an AI-driven inverse design paradigm for developing deployable thermo-mechanical metamaterial implants. By combining evolutionary algorithms (ES) with backpropagation neural networks (BP-NN), an inverse design model was constructed that could automatically optimize microstructure parameters based on target mechanical properties (e.g., bending stiffness). This method broke through the tradeoff between functionality and minimally invasive nature in traditional implant design, achieving highly personalized implant design. Furthermore, the authors proposed a “Clinical Information-Aided AI” (CIAI) design framework, introducing fuzzy Analytic Hierarchy Process (AHP) to integrate multiple objectives such as biocompatibility, feasibility, and precision, promoting the transition of AI design from single-property optimization to comprehensive clinical decision-making. This study provided new ideas and methodological foundations for the multiobjective, interdisciplinary application of AI in biomedical engineering. Another representative work is shown in Figure d, where Suh proposed an inverse design method combining interpretable machine learning (IML) with a multiobjective genetic algorithm (GA) for developing biodegradable magnesium-zinc-manganese-strontium-calcium (ZMJX) alloys possessing both high mechanical strength (UCS ≥ 240 MPa) and a suitable degradation rate (DR ≤ 1 mm/y). Using SHAP (Shapley Additive Explanations) for feature importance analysis, it clearly revealed that Zn is the most influential alloying element on both UCS and DR, enhancing model interpretability; and through data augmentation techniques, expanded limited experimental data (45 sets) to 1044 sets, effectively supporting the high-precision prediction of the neural network (R ² > 0.92). This study achieved an inverse mapping from performance targets to composition design, successfully screened 6 Pareto-optimal alloy formulations, 4 of which were experimentally verified to meet the dual requirements, providing a reliable AI-driven paradigm for biomaterials design under multiobjective.

5.3. Fast Optimization

Artificial Intelligence significantly accelerates biomaterial fabrication through intelligent algorithms. Process parameter optimization and performance optimization serve as core implementation pathways, enhancing R&D efficiency from both fabrication processes and material characteristics perspectives: process parameter optimization dynamically adjusts synthesis conditions to substantially reduce process exploration cycles, while performance optimization employs multiobjective tradeoff algorithms to simultaneously improve biological functionality and mechanical properties, thereby reducing traditional trial-and-error costs. The synergistic application of these two approaches establishes a comprehensive optimization system from synthesis to functionality, greatly accelerating the development of high-performance biomaterials. Performance prediction and optimization of biomaterials refers to the process of pre-evaluating their key characteristics (e.g., biocompatibility, degradation rate, mechanical properties, drug release profiles) through computational or experimental methods before they are put into lengthy biological experiments and clinical applications. Based on this, material formulations and process parameters are adjusted to achieve the desired performance targets. This stage is a core bridge connecting material design with practical application, aiming to reduce R&D failure risks and costs. ,

Traditional performance prediction and optimization primarily rely on researchers’ domain experience, simple statistical regression analysis, and mechanism models based on physical rules. Researchers often conduct experiments using the one-variable-at-a-time method, establishing linear or empirical formulas based on limited experimental data, and manually adjusting through repeated “preparation-testing” cycles. This method is not only time-consuming and costly but also heavily dependent on expert intuition. It struggles to handle the multicomponent, multiscale, and nonlinear complex relationships widespread in biomaterial systems, resulting in low optimization efficiency and a high tendency to fall into local optima.

AI empowerment brings a paradigm shift to performance prediction and optimization. Its core lies in using machine learning algorithms to learn from historical experimental data, high-throughput screening data, or molecular simulation data, building high-precision nonlinear mapping models (i.e., “performance prediction models”) from material composition/structure to final properties. Once the model is trained, researchers can input any formulation or process parameters and instantly predict its performance, thereby replacing a large amount of time-consuming and laborious experimental trial-and-error. Furthermore, AI can use optimization algorithms (e.g., Bayesian optimization, genetic algorithms) guided by target performance to automatically and inversely search for the optimal material formulation and preparation parameters, achieving intelligent “performance optimization.” ,

AI’s assistive role in this process is manifested in two aspects: first, acting as a “super calculator,” capable of quickly and accurately predicting the properties of unknown materials; second, acting as an “intelligent navigator,” able to automatically find the best path toward the performance goal within the vast “material parameter space,” guiding the direction of experiments. Its significant advantages include: First, greatly improving R&D efficiency, shortening the ″trial-and-error″ process that originally took months or even years to just days; Second, revealing deep-seated patterns, able to discover complex “structure–property relationships” that are difficult to recognize through traditional methods; finally, achieving global optimization, breaking through the limitations of human experience to explore comprehensively superior or even disruptive innovative biomaterial formulations in a broader design space, significantly accelerating their clinical translation process. ,

AI plays a key role in material performance prediction and optimization, enabling accurate prediction of key performance indicators and multiobjective collaborative optimization. As shown in Figure a, Xu proposed a “human-in-the-loop” hybrid method combining coarse-grained molecular dynamics (CGMD), machine learning (ML), and experimental validation for efficiently predicting and designing tetrapeptide hydrogels. Using ML algorithms like Support Vector Machine (SVM) for regression prediction of the aggregation propensity (AP) of peptide sequences, and constructing a classification model to generate a gelation correction factor Cg, thereby establishing a comprehensive scoring function APHC = AP′ × log P′ × Cg. Through three rounds of ML-experiment iterative optimization, the model achieved an 87.1% prediction accuracy for hydrogel formation in a library of 8000 tetrapeptides, significantly higher than traditional methods. This method demonstrates the efficiency and generalizability of AI in peptide material design, providing a new paradigm for the rational design of functional peptides and their biomedical applications. Li employed an XGBoost machine learning model to systematically analyze the effects of different preparation strategies (e.g., annealing, salting-out, solvent exchange) and PVA mass fraction on the mechanical properties of hydrogels. For the first time, the contribution of preparation strategy and mass fraction to performance was quantified: XGBoost feature importance analysis showed that the preparation strategy accounted for 73.79%, far exceeding the mass fraction (26.21%), clearly indicating that process parameters are the key influencing factors. The model showed high accuracy in predicting tensile strength, modulus, and elongation at break (e.g., MAE = 1.37). The research indicates that machine learning can effectively guide the rational design of high-performance pure PVA hydrogels, providing an interpretable, data-driven paradigm for biomaterials optimization. In Figure b, Jiang developed an AI-driven platform named “AMP-hydrogel-Designer” for the de novo design of novel antimicrobial peptide (AMP) hydrogels. It integrated various advanced technologies such as Generative Pretrained Transformer (GPT), prompt-tuning, knowledge distillation, and reinforcement learning (RL). Using a multiobjective reward function (including antibacterial activity, species-specific MIC prediction, and cysteine content), it optimized and generated a highly efficient thiol-containing antimicrobial peptide, AK15. The platform realized a full-process AI-driven workflow from sequence generation and optimization to experimental validation, completing peptide design in about 16 days, a task that traditionally takes months. The research shows that AI can not only efficiently generate peptide sequences with broad-spectrum antibacterial functions but also guide the construction of hydrogel materials with multiple functions like antibacterial and pro-healing, providing an innovative paradigm for automated, multiobjective optimal design in the biomaterials field. In Figure c, Bannigan systematically applied machine learning (ML) for the first time to predict drug release behavior and optimize formulation design of polymer-based long-acting injectables (LAI). By comparing 11 ML algorithms, LightGBM was found to perform best in predicting drug release. Nested cross-validation and SHAP interpretability analysis were used to ensure model robustness and interpretability. For the first time, prospective formulation design based on feature importance analysis was achieved, successfully guiding the experimental validation of “fast-release” and “sustained-release” PLGA microspheres. This study provides a data-driven AI paradigm for pharmaceutical formulation development, significantly reducing experimental trial-and-error costs. The optimization of preparation process parameters in biomaterials manufacturing refers to the systematic method of adjusting key operational conditions during material synthesis and processing (e.g., temperature, pressure, reaction time, feed ratio, stirring speed, cross-linker concentration, pH, drying method) to obtain material products with ideal properties, high reproducibility, and scalability. , The goal is to ensure the stability, repeatability, and feasibility for clinical translation of the final product’s performance. Traditional optimization of preparation process parameters heavily relies on the Trial-and-Error method and experience-based orthogonal experimental design. Researchers typically manually adjust one or a few variables while keeping others constant, conducting numerous repetitive experiments to observe performance trends, and use simple statistical methods (e.g., response surface methodology) to find a relatively optimal process window. This method is not only time-consuming and consumes large amounts of raw materials and labor costs, but its optimization efficiency is highly dependent on the operator’s personal experience. A more critical shortcoming is its difficulty in handling complex process systems with multiple parameters, nonlinearity, and strong coupling, easily missing interactions between parameters and thus falling into local optima, unable to find globally optimal process solution.

5.

AI applications in performance prediction and optimization: (a) workflow of coupled experimental and machine learning approach for discovering tetrapeptide hydrogels and their potential biological applications, (b) Schematic of the AI-guided design and performance of AI-AMP hydrogel, Reproduced or adapted with permission from ref Copyright 2025 John Wiley and Sons (c) Schematic demonstrating traditional and data-driven formulation development approaches for long-acting injectables.

AI-empowered process parameter optimization is a data-driven new paradigm. Its core is to treat the complex relationship between preparation process parameters (input) and final material properties (output) as a “black box,” and use machine learning algorithms (e.g., Random Forest, Gradient Boosting Machines, Neural Networks) to learn this mapping relationship from historical experimental data or actively generated data, building a high-precision process-performance prediction model. On this basis, intelligent optimization algorithms like Bayesian Optimization are used, guided by target performance (e.g., highest cell activity, fastest degradation rate), to automatically recommend the next set of process parameters most worth trying, thereby efficiently navigating the entire parameter space and approaching the global optimum. ,

AI’s assistive role in this process is manifested in two aspects: first, acting as a “virtual process engineer,” able to quickly predict potential outcomes for any combination of process parameters, greatly reducing the number of physical experiments; second, acting as an “intelligent navigator,” able to autonomously explore the process parameter space and proactively discover efficient preparation paths overlooked by human experience. , Its most significant advantages are: First, significantly improving optimization efficiency and reducing costs, able to quickly pinpoint the globally optimal process scheme with the fewest experiments, saving substantial time and resources; Second, revealing complex nonlinear patterns, able to deeply mine implicit interactions between process parameters and between parameters and performance, forming deep knowledge to guide production; finally, ensuring result reproducibility and robustnessthe optimum found by AI often lies within a stable performance plateau, more conducive to scaled production and clinical translation, ultimately promoting the transition of biomaterials preparation from an “art” to a “science.” Artificial intelligence demonstrates significant advantages in optimizing material preparation process parameters, enabling efficient analysis of complex coupling relationships between multiple parameters, markedly reducing experimental trial-and-error frequency and resource consumption, and achieving precise control and intelligent design of processes. Its core value lies in combining data-driven modeling, multiobjective optimization, and real-time monitoring to provide interpretable, generalizable decision support for the manufacturing process while enhancing production efficiency and product quality. As shown in Figure a, Chen developed an AI-assisted high-throughput printing condition screening system (AI-HTPCSS) to optimize 3D bioprinting parameters for rapidly obtaining hydrogels scaffolds with uniform structures. Combining a programmable pneumatic extrusion printing platform with a deep learning-based image recognition algorithm, it automatically identifies and classifies printing states (e.g., droplet, droplet line, continuous line) and quantifies line width uniformity; determines key printing parameter weights through Canonical Correlation Analysis (CCA) and multiple linear regression, achieving high-throughput, automated, and interpretable printing condition optimization. This system significantly reduced the cost and time associated with traditional manual trial-and-error, improved printing quality and efficiency, provided a new paradigm for intelligent and standardized biofabrication, and promoted the practical application of AI in tissue engineering and regenerative medicine. Moreover, Dai developed a method for constructing personalized oral soft tissue by combining artificial intelligence (AI) with 3D bioprinting (Figure b). Using an AI optimization platform based on Orthogonal Array Composite Design (OACD) and second-order quadratic equations, it efficiently predicted the effects of 81 printing parameter combinations (e.g., printing speed, pressure, nozzle size, ink concentration) on fiber diameter using only 25 sets of experimental data, significantly reducing the large number of experiments required by traditional trial-and-error (from 1440 to 25). The AI tools not only accurately screened ideal printing parameters but also deeply analyzed the interactive effects of multiple parameters, improving printing precision and shape fidelity. This method provides an interpretable, highly efficient AI-driven approach for biofabrication.

6.

AI Applications in Optimization of Preparation Process Parameters: (a) schematic diagram of the AI-HTPCSS for rapid screening of the optimized extrusion printing conditions of a given printer and biomaterial ink combination, Reproduced or adapted with permission from ref Copyright 2025 John Wiley and Sons (b) Schematic representing the reconstruction of mucogingival defects using autologous grafts and 3D bioprinting.

6. Future Development and Challenges

The deep integration of artificial intelligence in biomaterials preparation is leading the field into a new era of intelligent R&D. However, while embracing this paradigm shift, this interdisciplinary field also faces profound challenges related to technology transfer and paradigm fusion. A visual summary of the outlooks and challenges is provided in Figure . The core of future development will no longer be limited to the optimization of single algorithms but will focus on how to systematically solve the entire chain of complex problems from data to knowledge, from models to experiments, and from the laboratory to the clinic, ultimately achieving comprehensive empowerment of biomaterials innovation by AI in the truest sense.

7.

Outlooks and challenges of artificial intelligence-assisted biomaterials preparation.

6.1. Future Outlook

6.1.1. Comprehensive Upgrade of Intelligent Design Paradigms

In the future, AI will achieve a leap from “assisted design” to “autonomous creation.” By integrating generative adversarial networks (GANs), diffusion models, and reinforcement learning algorithms, AI will be capable of autonomously proposing novel biomaterial formulations with innovative structures and functions, thereby breaking through the limitations of traditional human-centric design paradigms. As highlighted in the literature, deep learning models such as convolutional neural networks (CNNs) and graph neural networks (GNNs) are already enabling the high-dimensional integration of multiomics data for biomedical applications. Tools like Protein MPNN have been successfully employed to design artificial spidroins with enhanced solubility and mechanical strength, while AlphaFold3 has revolutionized protein structure prediction and protein–protein interaction modeling. Looking ahead, AI will further embrace the concept of “computational biomaterials,” simulating and predicting structure–property-function relationships to accelerate the discovery of functional peptides, proteins, and other macromolecules. For instance, machine learning pipelines have been utilized to rapidly identify antimicrobial peptides from virtual libraries, demonstrating the significant potential of AI in rational biomaterial design. Future systems will integrate multisource dataincluding genomics, proteomics, imaging, and clinical outcomesto enable “inverse design” of biomaterials tailored to specific physiological or pathological contexts. Furthermore, AI-driven generative models can be deployed to design bioinspired materials that replicate complex natural architectures, such as multilayered scaffolds mimicking the epidermis-dermis interface of skin. The synergy between AI and high-throughput experimentation will also facilitate the discovery of novel bioactive compounds from natural sources, like polysaccharide derivatives with immunomodulatory functions for applications such as hair regeneration. This intelligent design paradigm will not only accelerate material innovation but also pave the way for developing personalized biomaterials capable of dynamically adapting to individual patient profiles and disease states.

6.1.2. Full Realization of Intelligent Manufacturing

The future of biomaterials fabrication is poised to enter an era of end-to-end intelligence. AI algorithms will oversee the entire production pipelinefrom raw material selection and synthesis processes to real-time monitoring and final product inspectionachieving autonomous optimization and adaptive adjustment. The literature notes that AI can assist in designing and optimizing biomaterial formulations, including nanoparticle systems for drug delivery. For example, machine learning has been applied to optimize the preparation of silica-coated gold nanorods for photothermal therapy. In the coming years, AI-driven microfluidic systems and 3D bioprinting platforms will enable the high-throughput, reproducible fabrication of personalized implants and tissue scaffolds. Real-time feedback from integrated sensors and imaging systems will allow AI to dynamically adjust processing parameters, ensuring consistent product quality and performance. Specifically, AI can optimize synthesis parameters for stimuli-responsive biomaterials, such as thermosensitive hydrogels used in injectable formulations for skin rejuvenation. Additionally, AI-powered robotic systems can automate the assembly of complex composite biomaterials, such as bioelectronic skins that integrate hydrogels, bacterial components, and sensing elements. This level of intelligent manufacturing will enhance production scalability, reduce batch-to-batch variability, and facilitate on-demand production of patient-specific biomaterials, ultimately lowering costs and improving accessibility.

6.1.3. Cross-Border Integrated Innovation Applications

AI will catalyze the deep integration of biomaterials with nanotechnology, information technology, and the internet of things (IoT), giving rise to a new generation of intelligent biomaterial systems. The convergence of AI with medical imaging and diagnostic devices is already improving the accuracy of disease detection and classification. Future smart biomaterials may incorporate biosensing and feedback mechanisms, enabling real-time monitoring of physiological states and on-demand release of therapeutics. For example, AI-designed nanoparticles could be engineered to respond to specific biomarkers within the tumor microenvironment, thereby enhancing the precision of cancer therapy. Furthermore, AI-powered robotic systems and biosensors could support remote health management, particularly for aging populations, by providing continuous monitoring and enabling early intervention. A notable example is the development of breathable wearable electronic devices that integrate sweat management with real-time biosignal monitoring, offering enhanced comfort and signal stability during physical activity. Additionally, AI-enabled electronic skin (e-skin) systems can mimic the multimodal sensing capabilities of human skin, enabling advanced applications in prosthetics, robotics, and human–machine interfaces. These integrated systems will not only advance diagnostic and therapeutic capabilities but also open new avenues for interactive and adaptive healthcare solutions.

6.1.4. Transition toward Sustainable Development

AI will accelerate the development and industrialization of green, sustainable biomaterials. By leveraging large-scale data sets and predictive models, AI can help identify biocompatible, biodegradable, and sustainably sourced materials. The literature emphasizes the role of AI in reducing resource-intensive experimental screening through in silico modeling and virtual libraries. For instance, machine learning has been used to predict the environmental and biological impacts of nanomaterials, guiding the selection of safer and more sustainable options. In the future, AI could also optimize synthesis pathways to minimize waste and energy consumption, contributing to a circular economy in biomaterial production. For example, AI algorithms can aid in designing biomaterials derived from renewable resources, such as polysaccharide-based hydrogels from marine algae or plant-derived polymers, which offer excellent biocompatibility and a low environmental footprint. Moreover, AI can optimize the lifecycle management of biomaterialsfrom raw material extraction to disposalensuring minimal ecological impact. The development of smart, biodegradable materials that degrade in response to specific environmental cues will further enhance sustainability. These efforts align with global initiatives in green chemistry and sustainable healthcare, positioning AI as a key enabler of eco-friendly biomaterial innovation.

6.2. Challenges

6.2.1. Data Quality and Standardization Dilemma

High-quality, well-curated data is the cornerstone for training robust AI models. However, the biomaterials field faces significant challenges including data scarcity, lack of standardization, and high heterogeneity. The literature points out that biomedical data are often multisource, complex, and nonstandardized, making integration a formidable task. For example, multiomics data from genomics, proteomics, and metabolomics vary in format, scale, and quality, necessitating sophisticated preprocessing and fusion techniques. The scarcity of publicly available, well-annotated data sets severely limits the development and validation of advanced AI models. Therefore, the data dilemma for biomaterials AI is systemic: it is rooted in the inherently small-sample nature of data, its multilevel heterogeneity (chemical, structural, imaging, biological), the ambiguity and subjectivity of evaluation end points, and the severe domain shift across the “in vitro–in vivo–clinical” pipeline. Establishing FAIR (Findable, Accessible, Interoperable, Reusable) principle-guided databases must proceed in parallel with community consensus on standardized experimental operating procedures, characterization protocols, and biological end point reporting norms. Simultaneously, leveraging physics-based simulations to generate synthetic data and developing cross-domain transfer learning techniques should be encouraged to alleviate data scarcity. Establishing standardized data protocols and shared repositories is therefore essential to advance AI applications in biomedicine. In skin-related biomaterials, data heterogeneity is further compounded by variations in skin types, disease states, and environmental factors. For instance, clinical data from wound healing studies may encompass diverse end points such as epithelialization rate, scar formation, and microbial load, often reported using different metrics and scales. To address this, international consortia and regulatory bodies should promote the widespread adoption of FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Additionally, generating high-fidelity synthetic data through physics-informed models or generative AI could supplement limited experimental data sets, though rigorous validation against real-world data remains crucial.

6.2.2. Model Interpretability and Reliability Issues

The “black box” nature of many complex AI models, particularly deep learning architectures, severely restricts their trustworthy application in the biomedical field. Researchers and regulators find it difficult to understand and trust AI systems whose decision-making processes lack transparency. The literature notes that model interpretability is critical for clinical adoption, yet many state-of-the-art models operate as opaque black boxes. For instance, variability in model architectures, hyperparameters, and training data can lead to nonreproducible or inconsistent results. Explainable AI (XAI) techniques are emerging to address this issue, but further research is needed to effectively link AI predictions with underlying biological or materials science mechanisms. Furthermore, models must be rigorously validated across diverse, independent data sets to ensure robustness, generalizability, and clinical reliability. In the context of skin biomaterials, this challenge is particularly acute when AI is used to predict complex, nonlinear biological responses, such as immune modulation or long-term tissue integration. For example, an AI model might accurately predict the mechanical properties of a hydrogel but fail to provide a mechanistic explanation for why certain cross-linking densities promote macrophage polarization toward regenerative phenotypes. The “black-box” problem in biomaterials directly challenges of scientific rigor and regulatory compliance. Future trustworthy AI must strive to achieve “regulatory-grade explainability.” This means that the output of XAI cannot remain at the technical level of feature importance ranking but should form a logically clear evidence chain. It should elucidate how the design variables of a material influence its safety and efficacy end points through known or newly discovered physical, chemical, and biological mechanisms. This requires the deep integration of XAI with Physics-Informed Neural Networks (PINNs) and domain knowledge graphs, ensuring that explanatory results can directly lead to testable wet-lab hypotheses. Only in this way can AI evolve from an assistive tool into a reliable codiscoverer and design partner capable of passing stringent regulatory scrutiny. Developing interpretable models that offer mechanistic insightssuch as highlighting key molecular descriptors, structural features, or signaling pathwayswill be essential for gaining clinician trust and meeting stringent regulatory standards. Robust validation frameworks incorporating in vitro, in vivo, and clinical data are necessary to ensure model reliability across different populations, conditions, and real-world scenarios.

6.2.3. Technology Integration and Talent Cultivation Challenges

AI-assisted biomaterials development is a highly interdisciplinary endeavor that requires deep, seamless collaboration among materials scientists, biologists, clinicians, and AI/computer science experts. The literature calls for concerted efforts to bridge the communication and knowledge gaps between experts from these diverse disciplines to foster integrated AI-biomedicine solutions. Currently, there is a significant shortage of professionals who are proficient in both the intricacies of biomedical sciences and advanced computational/AI methodologies. Dedicated educational programs, interdisciplinary curricula, and collaborative research frameworks must be developed and promoted to train the next generation of versatile researchers. Moreover, integrating sophisticated AI tools into existing clinical and laboratory workflows poses substantial practical, technical, and cultural challenges that require careful change management and user-centric design. For instance, the development of advanced e-skin devices necessitates the seamless integration of flexible electronics, biocompatible/polymeric materials, and AI algorithms for real-time signal processing. Achieving this requires close, iterative collaboration between electronic engineers, polymer chemists, and data scientistsa synergy often hindered by disciplinary silos, differing terminologies, and distinct research cultures. To overcome these barriers, academic institutions, research centers, and industries should establish cross-disciplinary training programs, joint research centers, and incentive structures that genuinely reward collaborative innovation. Additionally, developing user-friendly AI platforms with intuitive interfaces could lower the entry barrier for biomedical researchers and clinicians, enabling broader adoption and more effective utilization of AI in biomaterials R&D and clinical practice.

6.2.4. Ethical and Regulatory Difficulties

The use of AI, especially generative AI, in biomaterials design introduces complex ethical issues and novel regulatory challenges. The literature highlights growing concerns regarding patient safety, data privacy, algorithmic bias, and the ethical use of autonomous design systems in biomedicine. For example, AI systems intended for or influencing clinical decision-making must adhere to stringent regulatory standards for safety and efficacy, yet existing guidelines and frameworks from bodies like the FDA or EMA may not fully address the unique, dynamic aspects of AI-driven design tools and their lifecycle management. Critical issues such as algorithmic bias and fairness, accountability and liability for AI-generated material designs or clinical recommendations, and intellectual property rights for AI-invented materials remain largely unresolved. Artificial intelligence assisted biological manufacturing faces the problems of patentability, data ownership and ethical compliance of algorithm generation results. At the level of patent law, AI-derived technical solutions may be difficult to obtain patent protection due to the lack of “human invention” elements, and it is necessary to clarify the attribution of contribution through rights distribution agreements. At the ethical level, AI applications of technologies such as gene editing must comply with ethical norms such as the Declaration of Helsinki and avoid technology abuse. At the level of privacy and security, the collection, training and cross-border flow of biological data must comply with data protection regulations such as GDPR and guard against biosafety risks. Developing comprehensive, forward-looking ethical guidelines and adaptive, risk-proportionate regulatory frameworks will be crucial to ensure the safe, equitable, and responsible deployment of AI in healthcare. In dermatological and skin biomaterial applications, these challenges are amplified when AI is used to design implantable devices, long-term therapeutic systems, or aesthetic interventions, such as bioelectronic skins, smart wound dressings, or personalized cosmeceuticals. Regulatory agencies worldwide are still evolving their frameworks for evaluating AI as a medical device, a design tool, or even a coinventor. Furthermore, the application of AI in sensitive areas like aesthetic medicine raises additional ethical questions about fairness, access, affordability, and the potential for exacerbating unrealistic beauty standards or social inequalities. Proactive, inclusive engagement with ethicists, policymakers, patient advocacy groups, and the public will be necessary to develop balanced governance models that foster innovation while rigorously safeguarding patient welfare, equity, and transparency.

6.2.5. Algorithmic Bias and Fairness Issues

AI models can inadvertently learn, perpetuate, or even amplify biases present in their training data, which may lead to designed biomaterials being more suitable or effective for specific demographic groups while neglecting or underperforming for others. The literature warns that biased or nonrepresentative data can result in unfair, suboptimal, or even unsafe outcomes, particularly in diverse patient populations. For instance, if training data are predominantly sourced from certain ethnicities, age groups, or geographic regions, the resulting AI models may have poor generalizability and performance for underrepresented populations. Ensuring diversity and representativeness in data sets and incorporating fairness-aware algorithms during model development are essential steps toward equitable AI solutions. Ongoing monitoring and auditing of deployed AI systems in real-world clinical or production settings are necessary to detect, diagnose, and mitigate biases over time. In the domain of skin biomaterials, bias can readily arise from data sets skewed toward specific skin phototypes (e.g., Fitzpatrick I-III), ages, or genetic backgrounds, potentially leading to materials, dosages, or treatments that are less effective or even harmful for excluded groups. For example, a model trained primarily on data from lighter-skinned individuals may fail to accurately predict the therapeutic response or risk of adverse effects (like hyperpigmentation) in darker skin to certain photodynamic therapies or nanoparticle-based treatments. To combat this, researchers must actively curate diverse and representative data sets that encompass a wide spectrum of skin phenotypes, genetic backgrounds, comorbidities, and environmental exposures. Additionally, fairness metrics should be embedded into the model development and evaluation pipelines, and techniques such as adversarial debiasing, reweighting, or the use of synthetic minority data should be employed to ensure that AI-designed biomaterials deliver equitable benefits across all populations.

7. Conclusion

The deep integration of artificial intelligence in biomaterials preparation is leading the field through a paradigm revolution from “experience-driven” to “data-driven.” This article systematically reviews the four core applications of AI across the entire biomaterials R&D chain: High-Throughput Screening enables efficient mining and prioritization of vast material candidate systems through intelligent experimental design and data analysis; Inverse Design uses generative models and optimization algorithms to achieve targeted derivation and innovation from performance goals to material composition; Performance Prediction and Optimization provides reliable theoretical guidance for material formulation iteration by building accurate “composition-structure–property” mapping relationships; and Optimization of Preparation Process Parameters ensures the reliability, reproducibility, and efficiency of synthesis pathways through dynamic modeling and intelligent control. These four dimensions are interconnected, together forming a complete intelligent R&D closed loop of “design-prediction-preparation-validation,” significantly overcoming the inherent bottlenecks of traditional “trial-and-error” methodslong cycles, high costs, and low efficiency.

Although AI-assisted biomaterials preparation shows great application prospects, the field still faces many challenges on its path to maturity. Uneven data quality, lack of standardization, and insufficient sharing mechanisms constrain the performance ceiling of models; poor algorithm interpretability limits their acceptance in clinical translation; difficulties in multiscale modeling hinder accurate prediction from molecular design to macroscopic properties; and the lack of ethical norms and regulatory frameworks brings uncertainty to future applications. Solving these problems requires close collaboration among materials scientists, computational experts, clinical doctors, and regulators. Future research should focus on the following directions: developing Explainable AI (XAI) to enhance model transparency and trustworthiness; establishing standardized databases and open-source platforms to promote data sharing; combining multiscale modeling and digital twin technology to achieve full-process optimization; promoting the deep integration of automated experimental platforms and AI systems to achieve true “autonomous experimentation”; and improving ethical norms and regulatory frameworks to pave the way for clinical translation.

It is foreseeable that with continuous algorithm advancements, ongoing data accumulation, and deepening interdisciplinary collaboration, AI will ultimately achieve the leap from “assisted design” to “autonomous creation”, giving birth to a new generation of biomaterials with disruptive properties. These intelligent materials will not only possess excellent biological functions and mechanical properties but will also enable personalized customization, dynamic response, and intelligent therapy, ultimately promoting breakthrough progress in fields such as regenerative medicine, precision medicine, and intelligent diagnosis and treatment, bringing revolutionary changes to human health. AI-assisted biomaterials preparation is not only a technological innovation but also represents a shift in scientific research paradigms. As the technology matures and application scenarios expand, we are moving toward a new era of intelligent, personalized, and precise biomaterials, which will bring revolutionary changes to disease treatment, tissue repair, and health management.

§.

De Wei and Ze Wang contributed equally to this work.

The authors declare no competing financial interest.