This AI can read a brain MRI in seconds, spot emergencies instantly, and may redefine how neurological care is delivered.
Researchers at the University of Michigan have developed an artificial intelligence system that can analyze brain MRI scans and deliver a diagnosis in just seconds, according to a new study. The model identified neurological conditions with accuracy reaching 97.5 percent and was also able to determine how urgently patients needed medical care.
The research team says the technology represents a major advance in neuroimaging and could significantly change how hospitals across the United States interpret brain scans. The findings were published today (February 6) in Nature Biomedical Engineering.
“As the global demand for MRI rises and places significant strain our physicians and health systems, our AI model has potential to reduce burden by improving diagnosis and treatment with fast, accurate information,” said senior author Todd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.
Testing the Prima AI Model
Hollon named the new system Prima. Over the course of one year, his research team evaluated the technology using more than 30,000 MRI studies. The model was tested across over 50 different radiologic diagnoses related to major neurological disorders.
In these comparisons, Prima exceeded the performance of other leading AI systems designed for brain imaging. Beyond identifying disease, the model was also effective at ranking cases by urgency so that the most critical patients could be addressed first.
Certain neurological emergencies, including strokes and brain hemorrhages, require immediate attention. Hollon explained that in these situations, Prima can automatically notify medical providers so treatment can begin without delay.
The system was designed to direct alerts to the most appropriate subspecialist, such as a stroke neurologist or a neurosurgeon. Feedback is available as soon as a patient finishes their imaging exam.
“Accuracy is paramount when reading a brain MRI, but quick turnaround times are critical for timely diagnosis and improved outcomes,” said Yiwei Lyu, M.S., co-first author and postdoctoral fellow of Computer Science and Engineering at U-M.
“At key steps in the process, our results show how Prima can improve workflows and streamline clinical care without abandoning accuracy.”
What Is Prima and How It Works
Prima is a vision language model (VLM), a type of artificial intelligence that can process images, video, and text together in real time. While AI has been applied to MRI analysis before, the researchers say this approach stands apart.
Earlier systems were typically trained on carefully selected subsets of imaging data and designed to perform narrow tasks, such as detecting specific lesions or estimating dementia risk. Prima was built differently.
Hollon’s team trained the model using every available MRI collected since radiology records were digitized at University of Michigan Health. This included more than 200,000 MRI studies and 5.6 million imaging sequences. In addition to scan data, the system was fed patient clinical histories and the reasons physicians ordered each imaging study.
“Prima works like a radiologist by integrating information regarding the patient’s medical history and imaging data to produce a comprehensive understanding of their health,” said co-first author Samir Harake, a data scientist in Hollon’s Machine Learning in Neurosurgery Lab.
“This enables better performance across a broad range of prediction tasks.”
Addressing MRI Backlogs and Workforce Shortages
Worldwide, millions of MRI scans are performed each year, many of them focused on diseases of the brain and nervous system. According to the researchers, the growing demand for imaging far exceeds the supply of trained neuroradiologists.
This imbalance contributes to staffing shortages, diagnostic delays, and preventable errors. In some health systems, patients may wait days or longer to receive MRI results.
“Whether you are receiving a scan at a larger health system that is facing increasing volume or a rural hospital with limited resources, innovative technologies are needed to improve access to radiology services,” said Vikas Gulani, M.D. Ph.D., co-author and chair of the Department of Radiology at U-M Health.
“Our teams at University of Michigan have collaborated to develop a cutting-edge solution to this problem with tremendous, scalable potential.”
The Future of AI in Medical Imaging
Although Prima demonstrated strong performance, the researchers emphasize that the work is still at an early evaluation stage. Future studies will examine how incorporating additional patient details and electronic medical record data could further improve diagnostic accuracy.
This expanded approach reflects how clinicians already interpret imaging studies in real-world settings. While many AI tools currently used in health care focus on highly specific tasks, broader systems remain uncommon.
Hollon describes Prima as “ChatGPT for medical imaging,” noting that similar technology could eventually be adapted for other types of scans, including mammograms, chest X-rays, and ultrasounds.
“Like the way AI tools can help draft an email or provide recommendations, Prima aims to be a co-pilot for interpreting medical imaging studies,” Hollon said.
“We believe that Prima exemplifies the transformative potential of integrating health systems and AI-driven models to improve health care through innovation.”
Reference: “Learning neuroimaging models from health system-scale data” by Yiwei Lyu, Samir Harake, Asadur Chowdury, Soumyanil Banerjee, Rachel Gologorsky, Shixuan Liu, Anna-Katharina Meissner, Akshay Rao, Chenhui Zhao, Akhil Kondepudi, Cheng Jiang, Xinhai Hou, Rushikesh S. Joshi, Volker Neuschmelting, Ashok Srinivasan, Dawn Kleindorfer, Brian Athey, Vikas Gulani, Aditya Pandey, Honglak Lee and Todd Hollon, 6 February 2026, Nature Biomedical Engineering.
DOI: 10.1038/s41551-025-01608-0
Additional authors: Asadur Chowdury, M.S., Soumyanil Banerjee, M.S., Rachel Gologorsky, Shixuan Liu, Anna-Katharina Meissner, M.D., Akshay Rao, Chenhui Zhao, Akhil Kondepudi, Cheng Jiang, Xinhai Hou, Rushikesh S. Joshi, M.D., Volker Neuschmelting, M.D., Ashok Srinivasan, M.D., Dawn Kleindorfer, M.D., Brian Athey, Ph.D., Aditya Pandey, M.D., and Honglak Lee, Ph.D., all of University of Michigan.
This work was supported in part by the National Institute of Neurological Disorders and Stroke (K12NS080223) of the National Institutes of Health.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
This work was also supported by the Chan Zuckerberg Initiative (CZI), Frankel Institute for Heart and Brain Health, the Mark Trauner Brain Research Fund, the Zenkel Family Foundation, Ian’s Friends Foundation and the UM Precision Health Investigators Awards grant program.
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