AI-Enabled Digital Twins for Biomaterials: A Multiscale Framework

Background. Biomaterials are still largely designed around population-average physiological assumptions rather than patient-specific biology, contributing to revision-surgery rates of ۱۰-۲۰% within a decade for common orthopedic implants [۱,۲]. AI-enabled digital twins (DTs) — computational replicas coupled to real-time patient data — offer a route to closing this gap. Gap. Existing reviews treat AI in materials discovery and digital twins in biomedicine as separate literatures, without a shared framework for comparing how AI methods perform across the molecular-to-organ scale continuum specific to biomaterials. Contribution. We propose a four-tier scale taxonomy (molecular, micro, tissue, organ) that links each spatial scale to appropriate AI methods, data requirements, and validation strategies, and use it to organize a comparative, criterion-based assessment of six AI method families alongside the patient-specific imaging, omics, and biomechanical data streams needed to instantiate them. We illustrate the framework with a bone-scaffold case, summarize the FDA/EU regulatory pathway now emerging for continuously learning AI-DT devices, and close with a translational roadmap. The framework is intended as a common reference point for researchers building the next generation of patient-specific biomaterial digital twins.

کلیدواژه ها:

digital twin ، biomaterials ، artificial intelligence ، regenerative medicine ، patient-specific design ، physics-informed neural networks ، multiscale modeling ، foundation models