Foundation models for brain imaging: A systematic review
Abstract
Foundation models (FMs), large neural networks pretrained on extensive and diverse datasets, have revolutionized artificial intelligence and demonstrated significant promise in medical imaging by enabling robust performance with limited labeled data. Although numerous surveys have reviewed the application of FMs in healthcare, brain imaging remains underrepresented, despite its critical role in the diagnosis and treatment of neurological diseases using modalities such as magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET). To address this gap, we present the first comprehensive and curated review of FMs for brain imaging. We systematically analyze 161 brain imaging datasets and 143 FMs up to Jan, 2026, providing insights into key design choices, training paradigms, and optimizations driving recent advances. Our review highlights that the race for larger models has stabilized in 2026 towards more efficient models. FMs for brain imaging heavily rely on MRI (92%) and CT (57%) inputs, while PET imaging remains vastly underexplored (supported by only 15% of models). Our study also demonstrates architectural vulnerabilities caused by homogenization and lack of diversity, with Vision Transformers utilized in 48% of visual encoders, and models predominantly built by patching pre-existing natural image backbones like SAM (19%), and CLIP (12%) rather than utilizing native domain-specific 3D medical imaging innovations. For each of the…
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