Barts Life Sciences spearheads new cutting edge AI research | Our news

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Barts Life Sciences spearheads new cutting edge AI research

CAP-AI, a pioneering research programme in Artificial Intelligence (AI), is underway at Barts Life Sciences, placing healthcare in east London at the forefront of the AI and technology revolution.

Comprised of five projects, CAP-AI is London’s first AI-enabling programme focused on stimulating growth in London’s AI cluster using AI and machine learning to deliver innovative healthcare and services to improve outcomes for patients.

Project teams from Barts Health NHS Trust and Queen Mary University of London will support a London-based Small/Medium Enterprise (SME) to deliver their project, with the aim of creating a new product that can be commercialised.

Sven Bunn, Programme Director Barts Life Sciences, said: “This collaboration between the NHS, higher education and AI start-ups makes it possible for us to explore AI’s true potential in healthcare. Barts Health is best-placed to deliver revolutionary breakthroughs in AI, due to its diverse patient population serving around 2.5 million people who speak over 60 languages.

“The NHS Long Term plan outlines a much more central role for AI technologies in healthcare over the next decade. Our CAP-AI programme places Barts Health NHS Trust at the forefront of that mission and we’re proud to be leading the way for AI and machine learning in healthcare.”

Two projects are already underway – the first led by Vascular Consultant, Sandip Sarkar from Barts Health NHS Trust in partnership with AI-start-up Motilent. Together they aim to use AI to predict how congenital ascending aortic aneurysm, an unpredictable and potentially deadly condition, is likely to develop in patients – which would enable their care to be better managed and aortic ruptures detected ahead of time. Find out more by watching Mr Sarkar's video blog.

Another project, working with iPlato Healthcare and Queen Mary University of London, aims to use AI to improve the treatment options for the thousands of patients across the UK with musculoskeletal (MSK) problems. Machine learning algorithms will be used within the ‘myGP’ app to offer appropriate alternatives for treatment to improve patient experience, reduce costs and free up GP time.

Future projects, which are due to start over the coming months, will aim to use AI to improve analysis of cardiac MRI scans, monitor online health boards to improve recruitment of suitable patients for clinical trials, and to predict vascular complications for patients with diabetes.

Artificial intelligence has the potential to address important health challenges, however its advances have been limited by the quality and quantity of health data available. CAP-AI’s collaborative approach seeks to tackle these challenges head on by combining technical expertise, healthcare data knowledge and innovation. This should better enable exploration and trialling of AI for a range of healthcare and research purposes, including detection and diagnosis of disease, management of chronic conditions and improved delivery of health services.

Led by Capital Enterprise, in partnership with Barts Health NHS Trust and Digital Catapult, CAP-AI is jointly funded by the European Regional Development Fund (ERDF) and Barts Charity.

John Spindler, CEO of Capital Enterprise said: “Barts NHS Health Trust is enabling some of London’s most innovate startups in the ‘AI in Healthcare’ space to work on some very exciting and ground-breaking projects. Capital Enterprise is proud to be working with the Trust as part of the part-ERDF funded CAP-AI Programme and is excited to see the outcomes of these collaborations.”

Francesca Gliubich, Director of Grants at Barts Charity said: “Barts Charity is thrilled to support this project, bringing the future of healthcare to East London. Using the latest AI technology will help ensure that patients in our community are receiving the best treatments at the right time.”


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  1. Malcom Prowle Wednesday, 27 February 2019 at 03:05 PM

    Would this work also have applicability to the early diagnosis of Type A aortic dissections

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