The Radiological Society of North America launched a new venture arm, RSNA Ventures, and also tapped its first industry partner to give radiologists faster access to data at the point of care.
RSNA Ventures, which was unveiled last week, inked a strategic partnership with Rad AI to integrate RSNA's peer-reviewed knowledge, which goes back 100 years, directly into radiologists’ workflow through the company's generative AI solution. Through the collaboration, RSNA Ventures will deliver RSNA’s peer-reviewed content into Rad AI's solutions already in use across U.S. radiology practices and health systems, the organizations said.
RSNA Ventures was established as a subsidiary of RSNA to accelerate innovative ideas that enhance the practice of radiology, improve patient care and advance the field of medical imaging, according to Adam Flanders, M.D., who serves on the RSNA board of directors as the liaison for information technology.
"It’s meant to be a launchpad for new ideas that will drive meaningful impact in radiology and imaging," Flanders told Fierce Healthcare.
There is a widening gap between rising demand and limited workforce capacity in radiology. The U.S. faces shortages in a myriad of medical fields, including diagnostic radiology. The increasing number of imaging studies, owing to advancing technology and an aging population, is outgrowing the capacity of radiologists, researchers report.
Radiologists are facing increasing case volumes and complexity on top of a workforce shortage, Flanders, who is a neuroradiologist and professor of radiology and rehabilitation medicine and vice chair of imaging informatics at Thomas Jefferson University in Philadelphia, noted.
"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. And, importantly, in a way that doesn’t interrupt the workflow," he said.
The partnership’s first milestone is a product demo at the RSNA 2025 annual meeting in Chicago in late November.
Jeff Chang, M.D., co-founder of Rad AI, is one of the youngest U.S. radiologists in history. He started medical school at New York University at age 16, did graduate work in machine learning at the University of Edinburgh and also earned a MBA from UCLA Anderson. Chang completed a fellowship in musculoskeletal MRI and then worked overnights as an ER radiologist for 10 years.
"As part of that, I realized just reading 250,000-plus studies over the years that we needed more tools to help save time and reduce fatigue and burnout. There's a lot of manual steps in radiology. We're reading hundreds of exams a day," Chang told Fierce Healthcare.
Chang met Doktor Gurson, a serial entrepreneur, and they co-founded Rad AI in 2018 to build gen AI tools for radiologists to help streamline workflows. Prior to Rad AI, Chang co-founded and ran a Y Combinator startup for four years, Doblet, which was acquired in 2017.
Rad AI uses its gen AI models trained specifically for radiology and healthcare, along with a large proprietary radiology report data set, to deliver solutions that save physicians time, reduce burnout and improve patient care.
The company claims it works with more than 200 customers across hospitals, health systems and radiology groups in the U.S, accounting for nearly 50% of all U.S. medical imaging. Rad AI works with nine out of 10 of the largest radiology groups in the country.
The company says its gen-AI-based solutions can save radiologists more than 60 minutes per shift and cut dictation time by nearly half. Rad AI also reports that 84% of radiologists using its products cite reduced burnout and have dictated up to 35% fewer words.
Rad AI's solutions include Impressions, which automatically generates report impressions from dictated findings; Reporting, a solution that uses advanced machine learning algorithms and gen AI to quickly create accurate reports; and Continuity, which closes the loop on follow-up recommendations for significant incidental findings in radiology reports.
For RSNA Ventures, Rad AI's "radiologist-led product vision" made the company a good fit for the venture arm's first industry partner, Flanders said.
"The company’s 'radiologist-first' focus aligns well with RSNA Venture’s mission to develop solutions that improve the everyday practice of radiology. Second, because Rad AI provides workflow solutions for radiology reporting, they were uniquely positioned to integrate RSNA’s trusted and vetted content right into the radiology workflow without interrupting our work. It was imperative to find a partner that could help us support radiologists at the point of image interpretation and diagnosis without adding stops, clicks or cognitive burden," Flanders said.
He added, "As we got further into discussions on partnering, we saw great synergy around innovation and solving problems for radiologists."
The collaboration between RSNA Ventures and Rad AI is designed to close one of the biggest gaps in modern imaging: ensuring that radiologists have immediate access to trusted, peer-reviewed, relevant information at the moment of interpretation, the organizations said. Instead of relying on memory or manual searches, case-based insights can be automatically surfaced, helping radiologists manage growing workloads while maintaining the highest standards of care.
"As a radiologist, you're looking at the images. You're dictating a case. You have to spend most of your time dictating the case. But as part of that, you're also coming up with differential diagnoses based on what you're seeing. This allows you to automatically launch cases, differentials and additional information from across the literature, the many different journals that they publish and summarized case studies of these potential conditions, so you compare those images to what you're currently seeing," Chang said. "A radiologist can very easily be able to identify this is the most likely differential. This puts all that in one place, single click or no click at all, to be able to make it really easy for them to access while they dictate cases."
Radiologists have always been pioneers in adopting technology, Chang noted. “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."
Integrating RSNA's peer-reviewed content into Rad AI's solutions will help radiologists 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," Chang said.
"There is no shortage of challenges to solve in medicine or in the field of radiology today. Collaboration between practitioners, the end users, and those who make the products is critical for ensuring utility, feasibility, optimization and adoption of new solutions and products," Flanders said.
RSNA Ventures brings the strength, expertise and credibility of RSNA's membership and peer-reviewed content to Rad AI’s well-established in-workflow AI platform, he noted. "Each partner leverages its unique core strength to bring an incredibly powerful solution to the radiology workflow. Together, RSNA Ventures and Rad AI translate knowledge into practice," he said.
Rad AI has raised more than $140 million to date, including a $60 million series C round in January that boosted its valuation to $525 million. Investors backing the company include Transformation Capital, Khosla Ventures, WiL (World Innovation Lab), Artis Ventures, OCV Partners, Kickstart Fund and Gradient Ventures (Google's AI-focused fund).