Researchers at the University of Michigan have developed an AI system called Prima that interprets brain MRI scans in seconds, identifying neurological conditions with up to 97.5% accuracy. The tool also flags urgent cases like strokes and brain hemorrhages, potentially speeding up medical responses. Findings from the study appear in Nature Biomedical Engineering.
A team led by neurosurgeon Todd Hollon at the University of Michigan has introduced Prima, a vision language model designed to process brain MRI scans alongside patient histories. Trained on over 200,000 MRI studies and 5.6 million imaging sequences from University of Michigan Health, Prima integrates clinical data to diagnose more than 50 neurological disorders.
Over a one-year evaluation period, the system was tested on more than 30,000 MRI studies, outperforming other advanced AI models in diagnostic accuracy. It not only identifies conditions but also prioritizes cases requiring immediate attention, such as strokes, by alerting relevant specialists like stroke neurologists or neurosurgeons right after imaging.
"As the global demand for MRI rises and places significant strain on our physicians and health systems, our AI model has potential to reduce burden by improving diagnosis and treatment with fast, accurate information," Hollon said.
Co-first author Yiwei Lyu, a postdoctoral fellow in computer science and engineering, emphasized the balance of speed and precision: "Accuracy is paramount when reading a brain MRI, but quick turnaround times are critical for timely diagnosis and improved outcomes. At key steps in the process, our results show how Prima can improve workflows and streamline clinical care without abandoning accuracy."
Unlike previous AI tools limited to specific tasks, Prima handles a broad range of predictions by mimicking radiologists' methods. Data scientist Samir Harake noted, "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. This enables better performance across a broad range of prediction tasks."
Radiology chair Vikas Gulani highlighted its relevance amid growing MRI demand and shortages: "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."
The researchers plan to enhance Prima with more electronic medical record data and adapt it for other imaging types, like mammograms and X-rays. Hollon described it as "ChatGPT for medical imaging," positioning it as a supportive tool for clinicians.
