To address increasing imaging demands and rising radiologist workload, RSNA Ventures and Rad AI are collaborating to deliver trusted, case-based insights directly within diagnostic workflows. The integration will allow automated report drafting, follow-up management, and contextual decision support helping radiologists work faster and more consistently.
Glimpse:
To address increasing imaging demands and rising radiologist workload, RSNA Ventures and Rad AI are collaborating to deliver trusted, case-based insights directly within diagnostic workflows. The integration will allow automated report drafting, follow-up management, and contextual decision support helping radiologists work faster and more consistently.
RSNA Ventures, the newly launched venture arm of the Radiological Society of North America (RSNA), has announced a strategic collaboration with Rad AI to bring generative AI driven enhancements into radiology workflows.
The core goal of the partnership is to embed RSNA’s century-long repository of peer-reviewed radiology knowledge directly into Rad AI’s Reporting platform, giving radiologists real-time access to evidence-based case insights as they interpret images.
This move comes in response to a growing challenge imaging volumes are increasing faster than the radiology workforce can scale, amplifying the risk of burnout and potential diagnostic delays.
Rad AI’s suite already includes features like automatic generation of report impressions based on dictated findings, structured reporting aids, and follow-up management for incidental findings. Through this collaboration, such tools will now be enriched with contextual, peer-reviewed content drawn from RSNA’s trusted body of work.
According to Rad AI, their solutions can save radiologists over 60 minutes per shift, reduce dictation time substantially, and drive greater satisfaction and consistency in report generation.
In comments, Dr. Adam E. Flanders, RSNA Board liaison for IT, noted that this collaboration enables RSNA’s validated content to reach radiologists “seamlessly and exactly when they need it.”
Meanwhile, Dr. Jeff Chang, co-founder & Chief Product Officer of Rad AI, emphasized that embedding trusted knowledge into daily practice can improve the speed, consistency, and confidence of diagnostic decisions.
The first public showcase of this integration is expected at RSNA 2025, where live demonstrations will highlight how RSNA’s content can aid image interpretation via Rad AI’s platform.
“By connecting trusted RSNA insights with daily practice, radiologists can deliver faster, data-backed recommendations grounded in the best available evidence.”
By
HB Team
