Artificial intelligence-driven 3D bioprinting hydrogels for wound healing: From design to closed-loop therapy

Chronic and non-healing wounds represent a significant clinical burden, driving the need for advanced therapeutic strategies that transcend passive conventional dressings. This review highlights the transformative convergence of artificial intelligence (AI) and 3D bioprinting for intelligent wound repair. It systematically examines three core dimensions: deep learning for wound diagnosis and monitoring via image analysis and smart hydrogel sensors for real-time monitoring; AI-accelerated bioink design with de novo hydrogels and active peptides; and AI-assisted optimization of 3D bioprinting for fidelity and biocompatibility. Key advances highlight a shift from empirical to data-driven design, enabling fabrication of constructs that are structurally biomimetic and functionally responsive. However, challenges like data scarcity, model interpretability, and the integration of multi-scale biological processes persist. By synthesizing these developments, this review provides a foundational framework for understanding the current landscape. Its principal contribution is proposing a fully integrated, closed-loop design-print-monitor-learn system, offering a roadmap toward adaptive, personalized, and autonomous wound regeneration therapies.