Artificial intelligence-driven design of smart biomaterials for next-generation tissue engineering and personalized regeneration

Abstract

Artificial intelligence (AI) is increasingly being explored in tissue engineering as a data-driven tool for linking biomaterial composition, scaffold architecture, fabrication parameters, and biological responses. Machine learning (ML) and deep learning (DL) approaches have been applied to predict scaffold properties such as mechanical behavior, porosity, and degradation, and to support biomaterial selection and optimization of biofabrication parameters, including extrusion pressure, printing speed, and material formulation. AI-assisted imaging and sensing can also support in-line monitoring of printing quality and provide a basis for emerging feedback-enabled and adaptive biofabrication strategies. Beyond fabrication, data-driven and hybrid computational models are being investigated for analysis of cell–material interactions and tissue microenvironments, including spatial, mechanical, and transport-related phenomena. AI-assisted approaches have also been applied to nanocarrier formulation and drug-release prediction, where physicochemical and environmental variables can be integrated to support formulation design and forecast release behavior. Emerging digital-twin frameworks extend these concepts by combining patient-specific imaging, computational modeling, and AI-assisted design to support individualized scaffold development. However, most current applications remain computational, retrospective, in vitro, or preclinical, and high predictive performance does not necessarily indicate translational readiness. Limited and heterogeneous datasets, insufficient external validation, fabrication constraints, bioink rheology, crosslinking variability, cell viability, model interpretability, and regulatory requirements continue to limit reproducibility and clinical translation. This review therefore provides a pragmatic, application-oriented assessment of AI in tissue engineering, emphasizing demonstrated capabilities, current validation levels, and the remaining gap between computational prediction and reproducible, clinically relevant biofabrication.