Resolving the multiscale design trade-off in bone tissue engineering: from generative design to digital twins

Abstract The clinical translation of bone tissue engineering is constrained by a severe multiscale design trade-off: the requirement for load-bearing mechanical strength competes directly with the need for vascular-permissive porosity. Conventional approaches address scaffold geometry, material formulation, and fabrication in a sequential, decoupled manner and therefore fail to navigate this multi-objective conflict. To overcome this limitation, we propose an ‘AI-Driven Holistic Intelligence’ framework. It spans the entire pipeline from generative inverse design to digital twin (DT)-based predictive maturation. The framework integrates gradient scaffold optimization, closed-loop adaptive manufacturing, and virtual clinical trial within a unified workflow. This architecture is designed to co-optimize the boundary conditions of stiffness and perfusion simultaneously, rather than in isolation. Nonetheless, realizing this vision as a clinically viable autonomous regeneration pathway will require bridging persistent translational gaps in data standardization, algorithm interpretability, and regulatory alignment. Importantly, the core innovation of this framework is not merely to navigate the empirical ‘trade-off’ along a fixed Pareto front, but to resolve it through multiscale co-optimization that expands the accessible design space, enabling simultaneous improvements in mechanical strength and vascular perfusion beyond the limits achievable by conventional single-objective approaches.