BERKELEY, Calif., Nov. 10, 2021 -- Rad AI, the fastest growing radiologist-led AI company, today announced $25 million in Series A funding. The round was led by ARTIS Ventures with participation by several existing investors, including OCV Partners, Kickstart Fund, and Gradient Ventures (Google's AI-focused fund). The funding will drive further development and commercialization of Rad AI Omni and Rad AI Continuity, the company's first core offerings on its AI platform, and advance Rad AI's mission to empower radiologists with AI — saving them time, reducing burnout, and helping to improve the quality of patient care.

How Rad AI Helps Radiologists and Improves Patient Care

Founded in 2018, Rad AI has seen rapid adoption of its AI platform, and is already in use at seven of the 10 largest private radiology practices in the United States. Rad AI uses state-of-the-art machine learning to streamline repetitive tasks for radiologists, which yields substantial time savings, alleviates burnout, and creates more time to focus on patient care. Its first product, Rad AI Omni, saves radiologists an average of 60 minutes per day, and helps achieve up to 20% time savings per report.
In October, Rad AI was recognized as the Best New Radiology Vendor by AuntMinnie. AuntMinnie.com is the largest community website for medical imaging professionals worldwide, and the Minnies have been the premier awards event in radiology since 2000.

"At Rad AI, we've always believed that AI will augment and benefit radiologists, not replace them," said Doktor Gurson, co-founder and CEO of Rad AI. "Radiology is an extraordinarily complex field, of which image pattern recognition is only a small part. By building products that put the radiologist and patient first we've been able to break through the noise and focus on what really matters -- reducing radiologist fatigue and improving patient care."

Rad AI Omni automatically generates a customized impression from the findings and clinical indication dictated by the radiologist, using the most advanced neural networks. It learns each radiologist's language preferences from all of their prior reports, to create an impression that the radiologist can simply review and finalize. In addition, Rad AI Omni improves report accuracy and consistency by making sure to include significant incidental findings, answering the main clinical question, and providing consensus guideline recommendations for follow-up. The impression appears in the practice's voice recognition software as soon as the radiologist finishes dictating the findings, without any clicks, hotkeys or new windows.

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