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Gus Iversen, Editor in Chief | May 05, 2026
SimonMed is extending the use of artificial intelligence across its outpatient imaging network, integrating FDA-cleared tools into standard diagnostic exams to extract additional clinical data without requiring new scans.
The Scottsdale, Arizona-based imaging provider said the expanded platform applies AI to commonly performed studies, including CT and MR exams, with the aim of identifying risk factors earlier and providing more structured results. The update includes three offerings: Calcium Score+, CT Bone Density and MR Lumbar Spine+.
Each tool analyzes images patients are already receiving. Calcium Score+ uses AI to quantify calcified plaque in coronary arteries from CT scans, a known indicator of cardiovascular risk. CT Bone Density evaluates bone mineral density from CT images that include the spine, offering an alternative to dedicated DEXA scans for identifying osteopenia or osteoporosis. Both can be applied to non-contrast CT chest exams, including lung cancer screening, with CT Bone Density also usable in abdomen and pelvis studies.

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MR Lumbar Spine+ applies AI to lumbar MR exams, generating quantitative measurements, severity grading and annotated visuals to support interpretation and standardize reporting.
The company said the tools are embedded into existing workflows and do not add scan time or radiation exposure. Results are delivered through SimonMed’s digital platform, which includes patient-facing summaries and follow-up coordination options.
“AI is accelerating a fundamental shift in healthcare, from reacting to disease to managing health over time,” said Dr. John Simon, CEO and founder of SimonMed. “By embedding AI directly into routine imaging, we’re able to extract more meaningful insights from exams patients are already having and make those insights easier for patients to understand and act on.”
SimonMed operates more than 170 imaging centers across 10 states and employs over 300 board-certified radiologists. The company said the expansion is part of a broader effort to scale AI-enabled imaging in community-based settings and support earlier detection through routine care.