From pixels to patterns: the AI revolution in stem cell-derived models

Abstract

Artificial intelligence (AI) is rapidly transforming stem cell and developmental biology, offering new strategies to analyze, interpret and optimize complex, dynamic systems such as organoids and stem cell-derived embryo models. In this Perspective, we chart the integration of AI into image-based analysis of stem cell systems, highlighting how deep learning, convolutional neural networks and emerging foundation models enable automated classification, segmentation and phenotyping at increasing scale and precision. We showcase applications in phenotyping, drug screening and mechanistic discovery, including real-time fate prediction and the identification of hidden morphological signatures linked to differentiation and disease. Practical challenges, including limited annotated data, model interpretability and live imaging constraints, are examined alongside future opportunities, such as multimodal integration, real-time experimental steering and protocol optimization. Altogether, we argue that AI is not merely an analytical tool, but a discovery engine that enhances reproducibility, accelerates insight and brings us closer to a mechanistic understanding of self-organization in complex stem cell-derived systems. This Perspective examines the role of AI-based image analysis when applied to stem cell-derived models.