Artificial intelligence technology represents a huge opportunity for diagnostics in medicine: with the right training, AI systems can quickly process large numbers of scans and images, identifying problems with remarkable accuracy. But there is a problem – training AI is time consuming and labor intensive. Enter RedBrick AI, an American start-up, which today announces a $4.6 million funding round to accelerate its scale-up; its tools and technologies can make a huge difference, it believes.
“AI is remarkably effective at making diagnoses; Using AI, for example, you can automate 40% of breast cancer diagnoses,” explains RedBrick AI Manager and Co-Founder Shivam Sharma. “But there is a real challenge: these systems are not easy to build, and healthcare in particular poses unique problems.”
Simply put, training an AI system requires researchers to show it as much data as possible—images and scans if your goal is to train it to read them. Each scan needs to be labeled to tell the system what it represents—an image of a cancer-free patient, perhaps, or an image that includes a potentially troublesome area that needs to be examined—so the AI can learn about what it looks like.
The problem here, says Sharma, is that no one has developed tools to help clinicians annotate images quickly and easily so that large amounts of data can be fed into the AI system quickly. “Due to the complexity, size and unique nature of medical images, clinicians must resort to traditional and difficult-to-use clinical tools to perform annotations,” he explains.
In that regard, Redbrick AI’s unique selling point is that it has developed a set of specialist note-taking tools designed specifically for healthcare. It believes that by using the tools, clinicians and programmers can reduce the time it takes to train an AI system by as much as 60%.
It represents a significant breakthrough, and opens up the possibility of accelerating the use of AI in healthcare. The medical profession is very open to such applications. In 2021 alone, the US Food and Drug Administration approved 115 AI algorithms for use in medical environments, an 83% increase compared to 2018, but there is room to go much further and faster.
Redbrick AI believes it improves upon existing technology in several important respects. First, the tools are designed tailored to the medical sector, rather than relying on more generic techniques that do not always reflect the nuances and specialties of healthcare. In addition, the tools can be accessed quickly through the platform and can be used without prior training. The platform also includes a number of automation facilities, which can manage and accelerate workflows.
It is a value proposition that is quickly gaining traction in the healthcare sector, with customers from the US, Europe and Asia signing up during the company’s first trading year. Redbrick AI offers its tools through a software-as-a-service model, with clients paying monthly subscriptions, based on their user ID, for access to the platform.
“With the rapid growth of AI in clinical settings, researchers need excellent tools to build high-quality datasets and models at scale,” adds Sharma. “Our customers are at the forefront of this growth, pioneering everything from surgical robots to automated cancer detection.”
Today’s fundraising will help Redbrick AI reach even more of these customers over the next 12 months. Sharma expects to use some of the money raised to further develop the company’s tools. It has also earmarked funding for its go-to-market strategy, where Sharma sees room to work with a larger number of corporate clients – the big medical research and technology companies – as well as smaller teams of healthcare specialists.
The $4.6 million seed round is led by Surge, the scale-up program run by Sequoia Capital India, with participation from Y Combinator and a number of business angels.
Sharma and his co-founder Derek Lukacs are excited about the opportunity to scale the company faster. “In this room, everything starts and ends with the hospital,” says Sharma. “That’s the source of the raw data, but it’s also where our technology will ultimately have the biggest impact—providing better patient outcomes.”