Advances in 3D printed chips for platelet activation assessment through integration with artificial intelligence

Activated platelets are key players in many thrombotic and hemostatic disorders. However, conventional platelet function tests often fail to capture how platelets behave under dynamic flow conditions that closely mimic physiological blood flow. Advances in 3D printing and microfluidic design now enable fabrication of more physiologically relevant microvascular constructs that support controlled investigation of platelet activation under defined hemodynamic environments. When integrated with artificial intelligence (AI) approaches, including deep-learning-based image analysis and physics-informed modeling, these platforms move beyond descriptive measurements toward automated, quantitative, and mechanistically interpretable assessment of platelet behavior. This review critically synthesizes recent progress at the intersection of 3D printing, microfluidic platelet assays, and AI-enabled analytics, with an emphasis on microfluidic design principles, detection strategies, benchmarking requirements, and translational considerations specific to platelet mechanobiology. It highlights how geometric- and shear-resolved microfluidic assays generate high-dimensional datasets that motivate AI-based analysis and address current biological and clinical limitations. Emerging applications include mechanistic studies of shear-mediated thrombosis, high-throughput drug screening under flow, and exploratory approaches to thrombotic risk stratification and patient-specific platelet phenotyping. Key challenges for translation include standardizing benchmarking against reference assays, rigorously reporting fabrication and hemocompatibility parameters, and validating AI models across multiple devices and patient cohorts. While these technologies are best viewed as complementary to established platelet function tests, their integration with AI-driven analytics may have important implications for advancing vascular diagnostics and thrombosis modeling.