Imaging AI Only Matters When It Works in the Real World

Large, multi-site real-world studies are becoming the deciding factor in how hospitals buy imaging AI. Despite broad agreement that real-world evidence matters, few vendors have tested their systems at scale across heterogeneous, multi-site screening programs, where performance often differs from lab studies.

For years, vendors relied on retrospective test sets, reader studies and controlled research environments. Health systems are now demanding evidence from routine clinical practice across diverse patient populations and equipment.

DeepHealth’s recent ASSURE study, which the company reports involved about 579,000 women across 109 community imaging sites, is a prominent example of this shift. The company’s broader RSNA 2025 launch, spanning lung, thyroid, prostate, neuro and operational AI, shows it is positioning real-world evidence as a platform-level strategy.

DeepHealth reports that ASSURE evaluated an AI-supported workflow in community screening practice. According to the company, the AI-augmented “Safeguard Review” workflow increased cancer detection rate by 21.6% compared with standard 3D mammography and improved positive predictive value by 15%, while recall rates stayed within American College of Radiology guideline limits.