Thought
Leadership

Artificial intelligence in radiology has reached a turning point. Healthcare executives and informatics leaders are no longer asking if AI can analyze imaging data, they are asking how to measure its return on investment (ROI). With hundreds of FDA-cleared algorithms on the market, health systems often find themselves facing "AI fatigue." Purchasing individual licenses for disparate point solutions creates complex vendor management challenges, high software maintenance costs,

The promise of artificial intelligence in medical imaging is no longer a matter of future speculation; it is a present-day reality. From automated triage of acute cases to automated organ segmentation, AI algorithms offer unprecedented opportunities to accelerate care. Yet, for many radiology leaders and clinical informatics teams, a significant disconnect remains between purchasing AI capabilities and realizing their full clinical utility in daily operations. Algorithms that

In acute and high-volume clinical settings, minutes matter. Whether evaluating a suspected Large Vessel Occlusion (LVO), scoring Coronary Artery Calcium (CAC), or identifying subtle pulmonary nodules, clinical teams need reliable diagnostic data delivered with zero friction. Yet, many health systems encounter what industry leaders call the AI execution gap: the disconnect between an algorithm generating an output and that output being utilized in a clinical pathway. Bridging

In the push to modernize diagnostic imaging, health systems aren’t facing a shortage of artificial intelligence algorithms; they’re facing an integration crisis. Over the last decade, hundreds of specialized AI algorithms have entered the market, promising everything from faster stroke triage to automated cardiac scoring. Yet, for many enterprise radiology and imaging departments, bringing these tools into daily clinical practice has proven complex, fragmented, and cost-prohi