Artificial intelligence-assisted smart hydrogel bioinks in 3D bioprinting: design, optimization, and construct...

Hydrogel-based bioinks are central to three-dimensional (3D) bioprinting because they provide hydrated, cell-supportive microenvironments with tunable rheological, mechanical, and biological properties. Smart hydrogel bioinks further introduce stimuli-responsive and dynamic behaviors, but their development remains constrained by empirical trial-and-error workflows and weak integration among formulation design, printability, process monitoring, and post-print biological performance. This review develops an AI-assisted workflow framework for smart hydrogel bioink development rather than treating smart materials, algorithms, and autonomous laboratories as separate mature topics. We examine how artificial intelligence (AI) can support feature representation, property prediction, printability assessment, process optimization, monitoring, construct characterization, and iterative refinement. Particular emphasis is placed on distinguishing direct evidence in smart hydrogel bioinks from broader bioprinting evidence, adjacent-field methodological inspiration, and prospective autonomous concepts. We also clarify the boundaries among supervised prediction, Bayesian optimization, active learning, computer vision, feedback control, and AI-agent-assisted workflow coordination. Finally, we discuss validation, benchmarking, grouped data splitting, uncertainty estimation, out-of-distribution detection, and the need to connect early material and process descriptors with long-term biological function. Overall, AI-assisted methods can make hydrogel bioprinting more predictive and quality-oriented, but real-time closed-loop control and fully autonomous bioink laboratories remain prospective goals that require standardized datasets, validated biological endpoints, uncertainty-aware models, external validation, and human oversight.