SimonMed Imaging uses AI to greatly improve full-body MRIs

Artificial intelligence reduced scan times, sometimes by 30-50%. Diagnostic accuracy improved. And the chances of missed diagnoses were reduced, improving patient outcomes.

At SimonMed Imaging, the primary challenge with full-body MRIs was achieving both accuracy and efficiency.

Traditionally, full-body MRIs rely solely on radiologists to interpret vast amounts of imaging data – often thousands of images – which is both time-intensive and prone to variability and human error. Imaging and screening centers faced challenges of delays in delivering timely results due to the requirement of manual interpretation of results.

"Another key challenge was the potential for human error, especially when identifying subtle abnormalities that could be early indicators of disease," said Dr. Sean Raj, chief innovation officer at SimonMed Imaging. "Radiologists could sometimes miss minuscule abnormalities in scans – small details that, if overlooked, could escalate into significant health risks and potentially delaying critical diagnoses.

"Additionally, full-body MRIs traditionally take longer scan times, which not only affects patient comfort but also limits throughput for imaging centers and quality of imaging," he continued. "Without AI, the diagnostic workflow faced inefficiencies that could compromise early detection, patient outcomes and overall healthcare delivery."

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