A team led by Tak, Garomsa, and Zapaishchykova published a universal foundation model in Nature Neuroscience that generalizes human brain MRI analysis across sites and protocols. The model improves cross-cohort performance on segmentation and diagnostic tasks, addressing a common failure mode where AI models overfit single-center datasets. Authors validated the framework on diverse multi-site datasets and demonstrated transferability to downstream tasks without extensive retraining. The paper positions foundation models as a route to scalable, reproducible MRI workflows and highlights remaining gaps in rare‑disease performance and regulatory validation. Clarification: A foundation model is a large pretrained AI that can be fine-tuned for specific downstream tasks with less labeled data.