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

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
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understanding of self-organization in complex stem cell-derived systems