Rad AI Pulls In $4M Seed Funding Round

The round was led by Gradient Ventures, Google's AI-focused venture fund. Investors UP2398, Precursor Ventures, GMO Venture Partners, Array Ventures, Hike Ventures, Fifty Years VC and various angels also participated in this round.

Founder Dr. Jeff Chang, the youngest radiologist and second youngest doctor on record in the US, was troubled by high error rates, radiologist burnout, and rising imaging demand despite a worsening shortage of US radiologists, so he decided to pursue graduate work in machine learning to identify ways that AI could help. After he met serial entrepreneur Doktor Gurson, they created Rad AI in 2018 at the intersection of radiology and AI. Built by radiologists, for radiologists, Rad AI is transforming the field of radiology with the inside perspective as its driving force.

"Radiology is facing severe pressures that range from falling reimbursements to market consolidation. There is also a radiologist shortage that is exacerbated by rising imaging volumes nationwide. We help radiology groups significantly increase productivity, while reducing radiologist burnout and improving report accuracy. By working closely with radiologists, we can make a positive impact on patient care," said Dr. Chang.

Rad AI uses state-of-the-art machine learning to automate repetitive tasks for radiologists so they have more time to focus on what matters: accurate and timely diagnosis for patients. The first product automatically generates the impression section of radiology reports, customized specifically to the preferred language of each radiologist. Initial customers have shown significant reduction in radiologist burnout, error rates, and turnaround time – improving radiologists' well-being and patient care.

Rad AI's current partners include Greensboro Radiology, Medford Radiology, Einstein Healthcare Network, and Bay Imaging Consultants, one of largest private radiology groups in the United States, as well as other radiology groups that have yet to be announced. Product rollouts have demonstrated an average of 20% time savings on the interpretation of CTs and 15% time savings on radiographs – translating into an hour a day saved for each radiologist.

With this new capital, Rad AI will build out its engineering team and expand the rollout of its first product to more radiology groups and customers.

Zachary Bratun-Glennon, Partner at Gradient Ventures, added, "The team at Rad AI is uniquely suited to apply innovative technology to this field, with strong radiology and AI experts and firsthand knowledge of this market. It's exciting to see the quantitative benefits and positive feedback from their radiology customers, and we're looking forward to the impact of their future products."

Rad AI is participating in the upcoming RSNA 2019 Annual Meeting in Chicago later this month, located in the AI Showcase at #10514B (North Hall, Level 2). The team will also present and demo at the Nvidia, AWS, and M*Modal (3M) booths – #10939, #10942, and #6513.

About Rad AI
Founded in 2018 by Doktor Gurson and Dr. Jeff Chang, Rad AI uses machine learning to transform the practice of radiology. Its AI products are designed by radiologists, for radiologists. By streamlining existing workflow and automating repetitive manual tasks, Rad AI increases daily productivity while reducing radiologist burnout. In addition, Rad AI provides more consistent radiology reports for ordering clinicians, and higher accuracy for the patients it serves.
The company has raised over $4 million in funding and is based in Berkeley, CA.

About Gradient
Gradient Ventures is Google's AI-focused venture fund – investing in and connecting early stage startups with Google's resources, innovation, and technical leadership in artificial intelligence. The fund focuses on helping founders navigate the challenges in developing new technology products, allowing companies to take advantage of the latest advances in machine learning, so that great ideas can come to life. Gradient was founded in 2017 and is based in Palo Alto, California.