BERKELEY, Calif., Nov. 25, 2019 /PRNewswire/ -- Rad AI, a startup transforming radiology with the latest advances in technology, today announces its company launch and a $4 million seed round 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.

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