Cash injection for artificial intelligence in NHS to speed up diagnosis of cancer

NHS patients will gain from major improvements in technology to speed up the diagnosis of fatal diseases, such as cancer, due to further investment in the use of artificial intelligence across the NHS. 

A cash injection of £50 million will scale up the work of existing Digital Pathology and Imaging Artificial Intelligence Centres of Excellence, which were launched in 2018 to develop cutting-edge digital tools to improve the diagnosis of disease. 

The three centres set to receive a share of the funding, based in Coventry, Leeds and London, will deliver digital upgrades to pathology and imaging services across an additional 38 NHS trusts, benefiting 26.5 million patients across England. 

Health and Social Care Secretary Matt Hancock said: “I am determined we do all we can to save lives by spotting cancer sooner. Bringing the benefits of artificial intelligence to the frontline of our health service with this funding is another step in that mission.” 

Today the government has also provided an update on the number of cancer diagnostic machines replaced in England since September 2019, when £200 million was announced to help replace MRI machines, CT scanners and breast screening equipment, as part of the government’s commitment to ensure 55,000 more people survive cancer each year. 

A further 69 scanners have now been installed and are in use, ten more are being installed and 75 have been ordered or are ready to be installed. 

The new funding is part of the government’s commitment to saving thousands more lives each year and detecting three-quarters of all cancers at an early stage by 2028. 

National Pathology Imaging Co-operative Director and Consultant Pathologist at Leeds Teaching Hospitals NHS Trust Darren Treanor said: 

Professor Reza Razavi, London Medical Imaging and AI Centre for Value-Based Healthcare Director, said: “Artificial intelligence technology provides significant opportunities to improve diagnostics and therapies as well as reduce administrative costs. With machine learning, we can use existing data to help clinicians better predict when disease will occur, diagnosing and treating it earlier, and personalising treatments, which will be less resource intensive and provides better health outcomes for our patients.”