RSNA Ventures and Rad AI Partner to Embed Trusted Radiology Knowledge into Daily Workflows

RSNA Ventures, the innovation arm of the Radiological Society of North America (RSNA), has announced a strategic collaboration with Rad AI, a company specializing in generative AI for healthcare. The partnership aims to bridge the growing gap between imaging demand and workforce capacity while giving radiologists direct access to RSNA’s extensive peer-reviewed knowledge base within their existing reporting environments.

The collaboration responds to mounting pressures across radiology departments as imaging volumes continue to rise faster than the available clinical workforce. By combining Rad AI’s workflow automation with RSNA’s century-long knowledge repository, the partnership seeks to make radiology interpretation faster, more reliable, and more informed by evidence-based insights.

At the core of this initiative is the integration of RSNA’s trusted case-based data into Rad AI Reporting, the company’s structured reporting platform. This means that radiologists will no longer need to rely solely on memory or manual searches when evaluating complex cases. Instead, relevant peer-reviewed content and similar case data will automatically appear in real time at the point of image interpretation—reducing friction and cognitive load.

“Radiologists have always been pioneers in adopting technology,” said Jeff Chang, M.D., co-founder and chief product officer of Rad AI. “This partnership with RSNA Ventures gives radiologists an unprecedented advantage, bringing case-based insights directly into their workflow in real time, allowing them to stay focused on image interpretation. 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.”

By embedding this knowledge directly into reporting workflows, RSNA Ventures and Rad AI are positioning radiologists to manage increasing imaging complexity more effectively. The integration promises faster decision-making, greater diagnostic precision, and a smoother connection between clinical expertise and the ever-growing body of radiological research.

As AI continues to shape diagnostic medicine, this partnership underscores a broader shift toward combining algorithmic efficiency with human judgment—ensuring that trusted medical knowledge remains central to radiology’s digital transformation.

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