Artificial intelligence in 3D bioprinting and biofabrication: a validation-stringency assessment

Three-dimensional (3D) bioprinting and biofabrication increasingly use artificial intelligence (AI) and machine learning (ML) for bioink formulation, printability assessment, process monitoring, and post-print maturation. However, most studies train and test their models within a single batch, printer, or laboratory, leaving model behavior under changing conditions unclear. In this review, we address this gap with two complementary frameworks. First, we introduce a three-tier taxonomy of AI methods, classifying them by how they learn, what model is used, and what task is performed. Second, we apply a five-level Validation Ladder, conceptually related to staged-maturity frameworks used in engineering and clinical AI, that grades evaluation rigor from random internal splits (Level 0) to real-time deployment with predefined acceptance criteria (Level 4). We apply both frameworks to 40 primary studies across the bioprinting pipeline and discuss their implications across seven tissue systems: bone/cartilage, cardiac, hepatic, neural, skin, vascular, and tumor. Approximately 83% of these studies remain at Level 0, boundary Level 0-1, or Level 1; 15% reach Level 2 cross-condition testing, only one study (2.5%) reaches Level 3 multi-laboratory testing, and none reach Level 4. This pattern reflects experimental scope rather than method immaturity, since higher levels require data from multiple independent batches and printer configurations. We conclude that community benchmark datasets for printability prediction, defect detection, and organoid phenotyping-annotated with their tier and validation level-are the most direct route toward bioprinting AI that can be reproduced beyond a single laboratory.