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, and identify issues with remarkable accuracy. But there’s a problem – training the AI is time-consuming and laborious. Enter RedBrick AI, a US start-up, which is today announcing 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 in making diagnoses; using AI, you can automate 40% of breast cancer diagnoses, for example,” explains RedBrick AI CEO and co-founder Shivam Sharma. “However, there is a real challenge. these systems are not straightforward to build and healthcare in particular poses unique problems.”
In simple terms, to train an AI system requires researchers to show it as much data as possible – images and scans if your aim is to train it to read these. Each scan has to be annotated in order to tell the system what it represents – an image of a cancer-free patient, perhaps, or an image including a potential troublesome area that needs investigating – so that the AI can learn about what it is looking for for.
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 have to 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 annotation tools designed specifically for the healthcare profession. It believes that using its tools, clinicians and programmers are able to reduce the time it takes to train an AI system by as much as 60%.
That represents a significant breakthrough, 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 compared to 2018, but there is scope to go much further and faster.
Redbrick AI thinks it improves on the existing technology in several important respects. First, its tools are designed bespoke for 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 its platform and can be used without any prior training. Also, the platform includes a number of automation facilities, which can manage and accelerate workflows.
It’s a value proposition that is quickly gaining traction in the healthcare sector, with clients from the US, Europe and Asia signing up during the business’s first year of trading. Redbrick AI offers its tools through a software-as-a-service model, with clients paying monthly subscriptions, based on their user numbers, 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 in the vanguard of this growth, pioneering everything from surgical robots to the automated detection of cancers.”
Today’s fundraising should help Redbrick AI to reach even more of these customers over the next 12 months. Sharma expects to deploy some of the cash raised in developing the company’s tools even further. It has also earmarked funding for its go-to-market strategy, where Sharma sees scope to work with larger numbers of enterprise customers – the large 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 by the opportunity to scale the company more rapidly. “In this space, everything starts and ends with the hospital,” Sharma says. “It’s the source of the raw data, but it’s also where our technology will ultimately have the most impact – driving better patient outcomes.”