At SimonMed Imaging, the primary challenge with full-body MRIs was achieving both accuracy and efficiency.
THE CHALLENGE
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.”
PROPOSAL
The introduction of artificial intelligence into full-body MRI was designed to fundamentally enhance the accuracy, speed and performance of SimonMed’s advanced medical imaging.
“We projected AI would be transformative, aiming to revolutionize the way imaging data was processed and analyzed,” Raj explained. “We recognized AI could solve key challenges by automating image enhancement, reducing noise and refining resolution.
“AI algorithms were trained on vast imaging datasets to detect subtle patterns, highlight abnormalities and provide radiologists with an extra layer of precision,” he continued. “This technology was intended to reduce variability in readings, ensuring that small but significant findings were not missed.”
This technology incorporated at SimonMed’s imaging centers would ensure that significant findings are flagged earlier, enabling faster intervention and improving patient outcomes, he added.
“Our patients would no longer face the anxiety of delayed results or the uncertainty of inconclusive scans,” he said. “Instead, they would benefit from a system designed to prioritize precision, speed and reliability.
“Additionally, AI-driven image reconstruction techniques could enable faster scan times without compromising quality – directly improving patient experience and increasing scanner throughput,” he continued. “This integration positioned AI as not just an enhancement but an essential tool in modernizing full-body MRI.”
MEETING THE CHALLENGE
The imaging provider leveraged AI across multiple stages of the MRI process – image acquisition, processing and interpretation – optimizing both efficiency and diagnostic precision across all of its MRI gantries.
“AI-driven imaging protocols allowed us to reconstruct high-resolution images from under-sampled data, significantly reducing scan times while maintaining or even improving image quality,” Raj said. “This meant patients spent less time in the scanner, improving comfort, overall accessibility and even improving image quality.
“AI-powered image reconstruction enhanced scan clarity by reducing noise, correcting for motion artifacts and sharpening fine anatomical details,” he continued. “This ensured radiologists were working with the highest-quality images possible.”
AI-assisted software analyzes MRI data in real time, highlighting potential abnormalities and assisting radiologists in identifying findings that might otherwise be subtle or easily missed. The key benefit here is that AI doesn’t replace radiologists – AI enhances radiologists’ ability to diagnose quickly and accurately, Raj noted.
The technology was fully integrated into SimonMed’s PACS and reporting platforms, streamlining radiologists’ workflow.
“By combining state-of-the-art MRI technology with AI, we created a more precise, efficient and patient-friendly full-body imaging experience,” Raj stated.
RESULTS
The use of AI in full-body MRI scans has delivered significant results for the healthcare organization. First, it significantly reduced scan times, sometimes up to 30%-50% quicker, making the process faster and more comfortable for patients while increasing scanner availability so the organization can increase scanner access and availability to communities.
“This improvement was achieved through AI-powered image reconstruction, which enabled high-resolution imaging with less data return time,” Raj reported. “Second, diagnostic accuracy improved, especially in detecting subtle abnormalities like small tumors and microvascular issues.
“AI algorithms enhanced image analysis by identifying patterns and highlighting areas of interest for radiologists, reducing the chances of missed diagnoses and improving patient outcomes,” he continued. “These results underscore how our strategic implementation of AI is not just an efficiency tool but a critical asset in improving healthcare quality and patient care.”
ADVICE FOR OTHERS
“For healthcare organizations adopting AI for full-body MRI, prioritizing patients means ensuring the technology directly enhances their care,” Raj advised. “Start by implementing AI solutions that reduce scan times, which can make the process less stressful and more comfortable for patients, especially those who experience anxiety or discomfort during long procedures.
“Faster scans also mean shorter waiting times, improving patient output and reducing healthcare delays,” he added. “Additionally, AI should be viewed as a tool to augment, not replace, radiologists. The most successful implementations use AI to enhance diagnostic accuracy while keeping radiologists at the center of decision-making.”
Finally, ongoing QA, QC and refinement are essential, he advised.
“AI in medical imaging is evolving rapidly, and continuous investment in the latest advancements and embracing AI will ensure that organizations stay at the forefront and best position organizations to deliver cutting-edge, patient-centric care,” he concluded.
Follow Bill’s HIT coverage on LinkedIn: Bill Siwicki
Email him: bsiwicki@himss.org
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