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Foundation AI model uses MRI data to predict multiple brain disorders
https://medicalxpress.com/news/2026-02-foundation-ai-mri-multiple-brain.html
Artificial intelligence (AI) systems are computational models that can learn to identify patterns in data, make accurate predictions or generate content (e.g., texts, images, videos or sound recordings). These models can reliably complete various tasks and are now also used to carry out research rooted in different fields.
Over the past few decades, some AI models have proved promising for the early diagnosis and study of specific diseases or neuropsychiatric conditions. For instance, by analyzing large amounts of brain scans collected using a noninvasive technique known as magnetic resonance imaging (MRI), AI could uncover patterns associated with tumors, strokes and neurodegenerative diseases, which could help to diagnose these conditions.
Researchers at Mass General Brigham, Harvard Medical School and other institutes recently developed Brain Imaging Adaptive Core (BrainIAC), a large AI system pre-trained on a vast pool of MRI data that could be adapted to tackle different tasks. This foundation model, presented in a paper published in Nature Neuroscience, was found to outperform many models that were trained to complete specific medical or neuroscience-related tasks.
"There is a vast trove of data within the millions of brain MRIs performed each year in the United States," Benjamin H. Kann, senior author of the paper, told Medical Xpress. "Typically, these scans are analyzed by humans for a particular reason, but this only scratches the surface of the story that these scans might tell us about our patients. With AI and advanced computational imaging techniques, we can unlock much more information from these scans than ever before--which may lead to potent, clinically useful ways to track a variety of acute and chronic conditions, from stroke, to cancer, to dementia, as well as predict future risks for patients."
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Over the past few decades, some AI models have proved promising for the early diagnosis and study of specific diseases or neuropsychiatric conditions. For instance, by analyzing large amounts of brain scans collected using a noninvasive technique known as magnetic resonance imaging (MRI), AI could uncover patterns associated with tumors, strokes and neurodegenerative diseases, which could help to diagnose these conditions.
Researchers at Mass General Brigham, Harvard Medical School and other institutes recently developed Brain Imaging Adaptive Core (BrainIAC), a large AI system pre-trained on a vast pool of MRI data that could be adapted to tackle different tasks. This foundation model, presented in a paper published in Nature Neuroscience, was found to outperform many models that were trained to complete specific medical or neuroscience-related tasks.
"There is a vast trove of data within the millions of brain MRIs performed each year in the United States," Benjamin H. Kann, senior author of the paper, told Medical Xpress. "Typically, these scans are analyzed by humans for a particular reason, but this only scratches the surface of the story that these scans might tell us about our patients. With AI and advanced computational imaging techniques, we can unlock much more information from these scans than ever before--which may lead to potent, clinically useful ways to track a variety of acute and chronic conditions, from stroke, to cancer, to dementia, as well as predict future risks for patients."
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