Title : Artificial intelligence as a control layer in tissue engineering: toward cognitive biomaterials for autonomous regenerative therapies
Abstract:
Despite substantial advances in scaffold chemistry, architecture, and biofunctionalization, tissue engineering systems continue to be fundamentally constrained by open-loop design paradigms in which cellular responses are assumed to be predictable and static. Such assumptions are incompatible with the spatiotemporal heterogeneity, nonlinearity, and patient-specific variability that govern tissue regeneration in vivo. In conclusion, the limited clinical translation of engineered tissues is increasingly understood to reflect not a lack of material sophistication, but rather the absence of autonomous control frameworks capable of interpreting and responding to dynamic biological feedback.
In this review, the emerging paradigm of AI-integrated cognitive biomaterials is critically examined as a necessary evolution in regenerative medicine, enabling a transition from responsive yet preprogrammed scaffolds toward adaptive, self-regulating tissue engineering systems. Recent advances in stimuli-responsive polymers, mechanotransduction-informed scaffold architectures, and embedded bioelectronic interfaces are evaluated through the lens of closed-loop cell–material interactions. Particular emphasis is placed on conductive hydrogels, integrated micro- and nanosensors, and organic electrochemical transistors capable of continuously monitoring cellular metabolism, mechanical stress, and adhesion dynamics within engineered tissues.
In the absence of artificial intelligence, such sensing platforms are rendered functionally underutilized, as the complexity and dimensionality of biological data exceed the capacity of rule-based control strategies. Machine learning and adaptive control algorithms are therefore positioned as enabling technologies for real-time inference and autonomous modulation of scaffold properties, including stiffness, degradation kinetics, and bioactive ligand presentation, in response to evolving cellular states.
The concept of bio-digital twins is further explored as a translational framework linking in vitro tissue development with in vivo regenerative outcomes, facilitating predictive optimization of tissue maturation, vascularization, and functional integration prior to implantation. Current challenges related to sensor biocompatibility, data latency, algorithmic interpretability, regulatory classification, and manufacturing scalability are critically assessed to distinguish technical barriers from conceptual limitations.
By reframing biomaterials as adaptive cyber-physical systems rather than passive constructs, AI integration is positioned as a defining requirement for autonomous tissue engineering and the clinical translation of personalized regenerative therapies.
Keywords: Cognitive biomaterials; artificial intelligence; closed-loop biointerfaces; autonomous tissue engineering; mechanotransduction; bio-digital twins; regenerative medicine.
