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Narrative review demonstrates how deep learning automates fetal brain MRI processing in prenatal care, highlighting potential gains in diagnostic accuracy and clinical workflow efficiency.
While slice‐to‐volume registration and super‐resolution reconstruction laid the foundation for motion‐corrected 3D T2‐weighted fetal brain magnetic resonance imaging (MRI) more than two decades ago, advances in deep learning are now enabling automation across acquisition planning, segmentation, biometry, and image quality control. In this narrative review, we highlight these emerging techniques and analyse their strengths and limitations in the context of clinical translation. We examine the major barriers to widespread clinical adoption of artificial intelligence tools and outline future directions at the clinical interface that may further transform the diagnostic role of fetal MRI. Together, these developments underscore a shifting landscape towards more comprehensive, quantitative in‐utero assessment, with the potential to enhance diagnostic accuracy and workflow efficiency, and broaden the clinical applications of fetal MRI in prenatal care.
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Luis et al. (2026) studied this question.
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