A team published a foundation AI model in Nature Neuroscience that generalizes across institutions and scanner types to analyze human brain MRI, offering a scalable framework for cross‑site imaging tasks. The model, tested on diverse datasets, shows robustness to domain shifts that traditionally degraded model performance in multi‑center studies. Authors Tak, Garomsa, and Zapaishchykova report transferability across varied acquisition protocols and clinical indications, potentially accelerating multi‑site imaging trials and reducing site‑specific retraining. The model structure provides a common backbone for downstream tasks like segmentation, lesion detection, and biomarker extraction. Imaging teams, clinical trial sponsors, and AI vendors should assess this foundation model for harmonizing MRI endpoints, accelerating regulatory submissions that require reproducible, multi‑site imaging biomarkers. Source: Nature Neuroscience paper.