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PinPoint Data Science Raises $1 Million For Cancer Research

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Health technology startup, PinPoint Data Science has successfully raised over £1m as part of a Series A funding round. The startup was able to do this with support from the Angel Investment Network (AIN). AIN is the world’s largest angel investment platform, they pride themselves in providing the necessary investment to help entrepreneurs succeed.

 

Pinpoint Data Science

 

PinPoint was started in 2018 and now has a team of nine full time employees. The new fund will enable implementation trials starting mid-2020. It will include Research and Development (R&D), funding a larger full-time team, regulatory compliance, plus the purchase of new equipment and the development of new products.

 

CEO Giles Tully says:

 

“These funds will help our ambition of enabling doctors to make better, smarter and more efficient decisions.

 

PinPoint has already achieved nearly 25% rule out, which last year would have given over 500,000 patients peace of mind in a few days, instead of worrying for a few weeks and saved the NHS over £150m. Our technology will save lives, improve patient experience and significantly reduce costs.”

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PinPoint Data Science Has A Solution To A very Real Problem

In the words of the Head of Consultancy at AIN, Sam Louis, who led the fundraising:

 

“This is one of the most exciting businesses we have worked with in recent years. Like all the best startups, they have developed a solution to a very real problem. In this case, it’s a problem that’s very close to home for a great number of people.

 

Their solution using machine learning and AI proved hugely popular with investors and we were delighted we were the only organisation they worked with to raise the funds. It was really encouraging that we were able to source investors that were aligned with their vision and that they want to come onboard.”

 

On its Website, the company states its vision as wanting to cure cancer patients without side effects. This they aim to achieve using AI and machine learning.

 

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