Overview
- Researchers at Mass General Brigham report in Nature Neuroscience that BrainIAC extracts disease signals from routine brain MRIs, including brain age, dementia risk, tumor mutations and survival.
- The model was validated on 48,965 multiparametric scans across seven tasks spanning healthy and disease-containing images and varied clinical settings.
- Across applications, BrainIAC beat task-specific systems and publicly available foundation models, particularly when only limited labeled data were available.
- Built with self-supervised learning to learn generalized MRI representations, the model required minimal fine-tuning to adapt to tasks such as glioma segmentation and time since stroke onset.
- The team released BrainIAC for research use, and external clinicians voiced cautious optimism while urging evaluation in real-world workflows and broader testing before clinical use.