News | Artificial Intelligence | November 12, 2021

Led by ARTIS Ventures, funding will advance further development and commercialization of Rad AI’s machine learning platform

Rad AI, the fastest growing radiologist-led AI company, announced $25 million in Series A funding.

November 12, 2021 — Rad AI, the fastest growing radiologist-led AI company, 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 U.S. 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. 

“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 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.

“It's important to us to remain at the forefront of implementing groundbreaking technology to best serve our patients and provide the optimal environment for our radiologists to do their best work,” said Casey Schmitz, M.D., neuroradiologist and physician lead for the AI Workgroup at Inland Imaging. “Our radiologists are impressed with the efficiency gains, quality gains, and reduction in fatigue. Rad AI is an ideal partner that is both innovative and customer-focused and places a premium on developing long-term partnerships.”

Rad AI's second product, Continuity, closes the loop on follow-up recommendations for significant incidental findings in radiology reports. Using AI-driven automation, Continuity ensures that appropriate patient follow-up is communicated and completed. This improves patient outcomes, reduces health system liability, and drives new financial value for health systems and radiology practices. Continuity integrates directly into health systems' EMR, and also has a platform available for outpatient imaging.

Made for Radiologists, by Radiologists

Rad AI Co-founder Jeff Chang, M.D., is the youngest radiologist and second youngest doctor in U.S. history. After working over a thousand overnight shifts as an ER radiologist for the past decade, he clearly saw some of the biggest problems radiologists face - fatigue and burnout, errors in reporting, and a shortage of radiologists despite rising imaging volume. After pursuing graduate work in machine learning, he co-founded Rad AI to provide ways to help radiologists using the latest advances in AI.

“Radiology is central to medical diagnosis and patient care; the radiology report is key to correct diagnosis, appropriate treatment, and monitoring of disease progression. When it comes to high-quality patient care, radiologists make all the difference," said Chang. “By using AI to streamline radiologists’ workflow and reporting, our goal is to positively transform radiology with the latest advances in technology - keeping the radiologist top of mind and the patient at heart.”

“AI is now serving as a critical skill multiplier for physicians, allowing for significant improvements in patient outcomes and overall healthcare costs,” said Stuart Peterson, founder and managing partner of ARTIS Ventures. “Within healthcare, radiology is a key part of medical diagnosis and treatment, and ideally suited to leverage the power of AI and machine learning. Rad AI's recent and significant momentum is a function of these tailwinds and its unique position - it has been built for radiologists by radiologists, transforming the field with this inside perspective as its driving force. We believe they are set to unlock enormous value for the entire healthcare imaging ecosystem.”

For more information: www.radai.com and https://www.av.co/ 

More information on RSNA21 can be found here.

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