Rad AI clinches $50M to scale generative AI tech for radiologists

Rad AI, a startup that developed an AI platform for radiology, pocketed $50 million in fresh capital to expand global availability of its generative AI solutions.

Khosla Ventures led the series B round, with participation from WiL (World Innovation Lab) and existing investors Artis Ventures, OCV Partners, Kickstart Fund and Gradient Ventures (Google's AI-focused fund), among others.

The company has raised more than $80 million to date.

The latest round of funding will accelerate the development and worldwide deployment of the company's products as Rad AI expands its team, the company said.

Rad AI developed technology to make radiologists' workload easier by reducing the time spent on report documentation. It's estimated that radiologists spend 75% of their time dictating reports based on what they see on medical images, often creating reports for more than 100 patients each day.

The company says its solutions are already used by more than a third of all U.S. health systems and by nine of the 10 largest U.S. radiology practices.

Founder Jeff Chang, M.D., a radiologist, started the company because he was troubled by high error rates, radiologist burnout and rising imaging demand despite a worsening shortage of U.S. radiologists. Rad AI uses state-of-the-art machine learning and AI to automate repetitive tasks for radiologists and automate workflow for health systems. It can generate parts of the radiology report customized to the radiologist's language and style.

"At Rad AI, we've built the most widely adopted generative AI solutions in healthcare, saving physicians time and improving patient care," said Doktor Gurson, co-founder and CEO at Rad AI, in a statement. "Rad AI has become a mission-critical part of health system workflows over the past five years. This strategic funding round further cements our position as the leading AI-driven workflow platform in healthcare."

The company's Rad AI Reporting is an AI-enabled solution for radiology reporting workflow, and it also developed a patient follow-up solution called Rad AI Continuity.

Earlier this year, Rad AI teamed up with Google to use its cloud and large language models to streamline workflows and reduce radiologists' administrative burdens. As part of the partnership with Google, Rad AI will use the tech giant's cloud platform and AI tools, including MedLM, a family of foundation models fine-tuned for healthcare industry use cases, including Gemini-based models in the future. 

Rad AI touts that health systems using Rad AI's solutions are able to increase patient follow-up rates for actionable findings from 30% to more than 85%, helping ensure that patients' new cancers are diagnosed and treated promptly. Health system customers also can create reports twice as fast while reducing the number of words dictated in many cases by up to 90%, which can help improve radiologist fatigue and burnout. The technology also can reduce report error rates by nearly 50% for complicated cases, improving quality of care, according to the company.

More than 80% of all healthcare data originate from radiology, providing Rad AI with a massive trove of data to train its proprietary LLMs. Rad AI says its models are trained on some of the largest healthcare data sets in the world, including exclusive partnerships with many leading health systems and radiology practices.

"Rad AI's transformative reporting software—powered by their proprietary LLMs trained on some of the world's largest radiology datasets—materially decreases the time it takes for radiologists to go through their workload while reducing fatigue and burnout," said Alex Morgan, M.D., Ph.D., partner at Khosla Ventures, in a statement.