<p>Microphysiological systems (MPS), including organoids and organ-on-a-chip platforms, are advanced in vitro models that replicate human organ-level functions and provide physiologically relevant alternatives to animal testing. While their use in drug development, disease modeling, and personalized medicine is expanding, the complexity and heterogeneity of MPS-derived image data pose significant challenges for reproducibility, standardization, and cross-platform comparability. This review explores how artificial intelligence (AI) and machine learning can address these challenges by enabling scalable, automated analysis of spatiotemporal imaging data. Key tasks, such as image segmentation, phenotype classification, and feature extraction, are discussed in the context of emerging AI models, including convolutional neural networks and transformer-based architectures. Application areas, such as vascular network quantification and organoid morphogenesis analysis, demonstrate the value of AI in reducing subjectivity and accelerating interpretation. Finally, future directions are outlined, including the integration of imaging with multi-omics data, the adoption of explainable AI frameworks, and the development of digital twins to enhance the predictive and regulatory utility of MPS platforms.</p> Graphical abstract <p>AI/ML-driven standardization of image analysis in microphysiological systems</p> <p>AI-enhanced image analysis enables standardized, scalable interpretation of complex biological data from microphysiological systems. This review highlights how deep learning-based segmentation and feature extraction improve reproducibility and support translational applications in drug development and personalized medicine.</p>