Explainable Artificial Intelligence in Biomaterials Design: Toward Trustworthy, Interpretable, and Clinically Translatable Biomedical Engineering Systems

Artificial intelligence has become deeply embedded in biomaterials science, driving advances in property prediction, biocompatibility assessment, drug delivery optimization, and implant design. Yet the very sophistication that makes modern deep learning models so powerful- multi-million parameter neural networks trained on high-dimensional datasets-simultaneously makes their decision-making processes opaque. This opacity is not merely an intellectual inconvenience: in clinical biomaterials applications, it poses a genuine risk. A clinician who cannot understand why an AI model recommends a particular scaffold formulation cannot meaningfully evaluate that recommendation, integrate it with clinical judgment, or identify when the model is extrapolating beyond its training domain. Regulators at the FDA and EMA are increasingly demanding that AI medical devices provide explanations for their outputs-a requirement that the biomaterials community must address proactively. Explainable artificial intelligence (XAI) encompasses a diverse toolkit of methods designed to make AI predictions interpretable to human users without sacrificing or with minimal sacrifice to predictive accuracy. This review provides the first comprehensive synthesis of XAI applications in biomaterials design, spanning the fundamental taxonomy of explainability methods (intrinsic versus post-hoc; global versus local), their application to the major prediction tasks in biomaterials science (polymer property prediction, toxicity assessment, degradation modeling, drug delivery optimization, and surface engineering), and the emerging frameworks for quantifying and benchmarking explanation quality. We critically examine the accuracy-interpretability trade-off that pervades the field, present case studies demonstrating the practical value of XAI in uncovering previously unknown structure-property relationships, and outline physics-informed explainable AI pipelines that combine the mechanistic transparency of physics-based models with the predictive power of data-driven approaches.

کلیدواژه ها:

explainable AI ، interpretable machine learning ، biomaterials ، SHAP ، LIME ، biocompatibility ، structure-property relationships ، regulatory compliance ، clinical decision support ، transparency