Accelerating the Use of AI in Health with Redbrick AI’s Training Tools

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 is a problem – training AI is time consuming and laborious. Enter Redbrick AI, a US start-up that is today announcing a $4.6 million funding round to accelerate its expansion; It believes that its tools and technologies can make a huge difference.

“AI is remarkably effective at making diagnoses; Using AI, you can automate 40% of breast cancer diagnosis, for example,” explains Shivam Sharma, CEO and co-founder of Redbrick AI. “However, there is a real challenge: These systems are not straightforward to manufacture and health care in particular poses unique problems.”

In simple terms, training an AI system requires researchers to show it as much data as possible – pictures and scans if your aim is to train it to read these. Each scan must 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 investigation – so that the AI ​​can know what he is looking for.

The problem here, says Sharma, is that no one has developed tools to help clinicians quickly and easily annotate images so that large amounts of data can be quickly fed into AI systems. “Due to the complexity, size and unique nature of medical images, clinicians have to rely on traditional and difficult-to-use diagnostic tools to perform annotations,” he explains.

In this regard, Redbrick AI’s unique selling point is that it has developed a set of expert annotation tools specifically designed for the healthcare profession. It believes that by using its tools, practitioners and programmers can reduce the time it takes to train an AI system by up to 60%.

This represents a significant breakthrough, opening up the potential to accelerate the application of AI in healthcare. The medical profession is very open to such applications. In 2021 alone, the US Food and Drug Administration is set to approve 115 AI algorithms for use in medical environments, an 83% increase from 2018, but there is room to go much further and faster.

Redbrick AI thinks it improves on existing technology in several important respects. First, its tools are designed for the medical field, rather than relying on more generic technologies that don’t always reflect the nuances and specialties of healthcare. Furthermore, the tool can be quickly accessed through its platform and can be used without any prior training. Plus, the platform includes a number of automation features that can manage and accelerate workflows.

It is a value proposition that is rapidly gaining traction in the healthcare sector with customers in the US, Europe and Asia during its first year of business. Redbrick AI provides its tools through a software as a service model, with customers paying a monthly subscription, based on their user count, for access to the platform.

“With the rapid development of AI in clinical settings, researchers need excellent tools to build high-quality datasets and models,” says Sharma. “Our customers are at the vanguard of this growth, leading everything from surgical robots to automated cancer detection.”

Today’s fundraising will help Redbrick AI reach more such customers over the next 12 months. Sharma expects some of the cash raised to further develop the company’s equipment. It has also earmarked funding for its go-to-market strategy, where Sharma sees scope to work with a large number of enterprise customers – large medical research and technology companies – as well as smaller teams of healthcare specialists.

The $4.6 million seed round is led by Surge, a scale-up program led by Sequoia Capital India, with participation from Y Combinator and multiple business angels.

Sharma and his co-founder Derek Lucas are excited by the opportunity to scale the company even faster. “In this place, everything begins and ends with the hospital,” says Sharma. “This is a source of raw data, but it is also where our technology will ultimately have the greatest impact – achieving better patient outcomes.”

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