RSNA Ventures Partners with Rad AI to Integrate Generative AI into Radiology Workflow

By embedding trusted, case-based insights into Rad AI's platform, the partnership seeks to enhance efficiency, reduce workload, and improve patient care.

RSNA Ventures, the innovation arm of the Radiological Society of North America (RSNA), has announced a partnership with Rad AI, a leader in generative AI for healthcare, to integrate over 100 years of peer-reviewed radiology knowledge directly into radiologists' workflows.

Reportedly, with this collaboration, the company aims to address the mounting pressure on radiologists as imaging volumes rise faster than the workforce can grow.

By embedding trusted, case-based insights into Rad AI's platform, the partnership seeks to enhance efficiency, reduce workload, and improve patient care.

Rad AI’s portfolio comprises a suite of generative AI solutions designed to optimize radiology workflows.

It automatically generates report impressions from dictated findings and leverages advanced AI algorithms to help radiologists create accurate reports, which is expected to ensure efficiency.

Further, the platform manages follow-up recommendations for incidental findings to ensure consistent patient care.

Reportedly, the company currently collaborates with over 200 hospitals, health systems, and radiology groups across the United States, accounting for nearly 50% of all medical imaging nationwide, including nine of the ten largest radiology groups.

According to Rad AI, its tools can save radiologists more than 60 minutes per shift, cut dictation time by half, and significantly reduce burnout, with 84% of users reporting improved workflow satisfaction.

Speaking about the partnership, Adam E. Flanders, M.D., RSNA Board liaison for information technology, said, “This collaboration enables RSNA Ventures to bring RSNA’s trusted knowledge and vetted resources directly to the radiologist, seamlessly and exactly when they need it.”

Further, Jeff Chang, M.D., co-founder and chief product officer of Rad AI, while emphasizing the importance of integrating trusted knowledge into daily practice said, "By connecting trusted RSNA knowledge with daily practice, radiologists can deliver rapid, data-backed recommendations to providers and answers to patients faster, with greater confidence and with assurance that decisions are grounded in the best available peer-reviewed knowledge."

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