Artificial intelligence technology represents a huge opportunity for diagnostics in medicine: with the right training, AI systems can rapidly process large numbers of scans and images, and identify problems with remarkable accuracy. But there is a problem: training AI is time-consuming and laborious. Enter RedBrick AI, an American start-up, which today announces a fundraising of 4.6 million dollars to accelerate its scale; his tools and technologies can make a huge difference, he believes.
“AI is remarkably good at making diagnoses; using AI, you can automate 40% of breast cancer diagnostics, for example,” says CEO and co-founder of RedBrick AI, Shivam Sharma. “However, there is a real challenge: these systems are not simple to build and healthcare in particular poses unique challenges.”
Simply put, to train an AI system, researchers need to show it as much data as possible – images and scans if your goal is to train it to read them. Each scan should be annotated to tell the system what it represents – an image of a cancer-free patient, perhaps, or an image including a potentially troublesome area that needs to be investigated – so the AI can know what what she is looking for.
The problem here, Sharma says, 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 rely on traditional and difficult-to-use clinical tools to perform annotations,” he explains.
In this regard, Redbrick AI’s unique selling point is that it has developed a set of specialized annotation tools designed specifically for the healthcare profession. He estimates that by using his tools, clinicians and programmers are able to reduce the time it takes to train an AI system by up to 60%.
This represents a significant step forward, opening up the possibility of accelerating the application 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 from 2018, but much more can be done and faster.
Redbrick AI believes it improves on existing technology in several important ways. First, its tools are tailor-made for the medical sector, rather than relying on more generic techniques that don’t always reflect the nuances and specialties of healthcare. In addition, the tools are quickly accessible via its platform and can be used without any prior training. Also, the platform includes a number of automation facilities, which can manage and speed up workflows.
It is a value proposition that is rapidly gaining momentum in the healthcare industry, with customers from the United States, Europe and Asia signing up within the first year of operation. company. Redbrick AI offers its tools through a software-as-a-service model, with customers paying monthly subscriptions, based on their number of users, to access the platform.
“With the rapid growth of AI in clinical settings, researchers need excellent tools to create 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 fundraiser should help Redbrick AI reach even more of these customers over the next 12 months. Sharma plans to deploy some of the funds raised into further development of the company’s tools. It has also earmarked funds for its go-to-market strategy, in which Sharma plans to work with a larger number of corporate clients – large medical research and technology companies – as well as smaller research teams. health specialists.
The $4.6 million round is led by Surge, the scaling program managed by Sequoia Capital India, with participation from Y Combinator and several business angels.
Sharma and co-founder Derek Lukacs are excited about the opportunity to scale the business faster. “In this space, everything starts and ends with the hospital,” Sharma explains. “It’s the source of the raw data, but it’s also where our technology will have the most impact, improving patient outcomes. »