The NHS has announced its Artificial Intelligence Awards, aiming to bring more “life-saving, artificial intelligence innovation” to the organisation.

Launched by health and social care secretary Matt Hancock, the awards hope to “see cutting-edge technology fast-tracked to the frontline”.

The NHS says that AI is already in use across the organisation, successfully predicting cancer survival rates and cutting the number of missed appointments, but the Awards will ensure that such advances are deployed more widely across the health service, as well as ensuring world-leading technology is available at scale to all staff and patients.

When announcing the competition, Matt Hancock stressed that the agenda is “not about technology, it’s about people,” before stating “the best kind of tech is the technology you barely notice because it just works.”

He continued to say: “It’s the tech that gets you away from the screen and lets you make eye contact with the patient in front of you. It’s the kind of tech that helps humanise a difficult and demanding environment, by freeing you up to do more of the work you love. Giving clinicians back the gift of time and allowing them to care.

“That’s what we’re aiming for, it’s what clinicians are crying out for, it’s what patients expect and it’s what will bring our NHS into the 21st century.”

The competition entries are open from Tuesday 28 January for the next five weeks, and the health secretary is urging companies to bid for a share of £140 million to launch their innovation across the health service, with funding awarded based on their potential to save lives and free up staff time and help deliver.

Run by the Accelerated Access Collaborative in partnership with NHSX, the award will form part of the £250 million AI Lab announced by the Prime Minister last year.

Revealed in August last year, the proposed AI Lab could improve cancer screening by speeding up the results of tests, including mammograms, brain scans, eye scans and heart monitoring and use predictive models to better estimate future needs of beds, drugs, devices, or surgeries.