London hospitals to use AI to address poor A&E waiting times and diagnose cancer
AI is being leveraged in a bid to try and revolutionise the NHS. Under a new partnership between University College London Hospitals (UCLH) and the Alan Turing Institute, doctors will be replaced by artificial intelligence in some instances, in an attempt to build efficiency.
Under the new scheme, AI could be responsible for diagnosing cancer on CT scans and deciding which A&E patients are seen first, among other things, at one of the UK’s largest hospitals.
The aim of the partnership is to apply ‘game-changing’ benefits of machine learning to the NHS on a previously unprecedented scale.
The director of research at University College London Hospitals NHS Foundation Trust, Prof Bryan Williams, told The Guardian the move was “going to be a game-changer.”
“You can go on your phone and book an airline ticket, decide what movies you’re going to watch or order a pizza … it’s all about AI,” he said. “On the NHS, we’re nowhere near sophisticated enough. We’re still sending letters out, which is extraordinary.”
The amount that UCLH is investing is undisclosed, but had been called “substantial”. AI could help improve healthcare by diagnosing disease, identifying people at risk of illness and directing resources.
Firstly, UCLH hopes that AI can reduce A&E waiting times, which are falling below standards. In fact, March figures show that just 76.4% of patients needing urgent care were treated within four hours at hospital A&E units in England. This is the lowest since 2010; when records began.
Prof Marcel Levi, UCLH’s chief executive, commented on this: “Our performance this year has fallen short of the four-hour wait, which is no reflection on the dedication and commitment of our staff,
“[It’s] an indicator of some of the other things in the entire chain concerning the flow of acute patients in and out the hospital that are wrong.”
This move doesn’t come without its concerns spanning across privacy, cybersecurity and the changing role of health professionals. The worry is that AI could lead to the development of “learned helplessness”, where people become so reliant on automated instructions that they abandon common sense.
AI may be right 99.9% of the time, but health professionals need to stay alert for that 1% of the time when it may make a mistake, according to Holmes.
He said: “Once in a blue moon it makes a howler. You want to quantify the risk of that.”
To avoid repeating mistakes, such as that when the Royal Free Hospital handed over personal data of 1.6 million patients to DeepMind, algorithms will be trained on the hospital’s own servers and private companies will not be involved, according to Holmes.
The idea is that AI will help identify who needs critical care, and can help doctors and nurses gravitate toward where they are most needed. Regardless, there is still a need for the human touch.
Levi said: “Machines will never replace doctors, but the use of data, expertise and technology can radically change how we manage our services – for the better.”
Other tasks that AI could undertake includes identifying patients who are are likely to fail to attend appointments. Machine learning could also be applied to to the analysis of the CT scans of 25,000 former smokers who are being recruited as part of a research project and perhaps automate cervical smear tests.
This partnership comes as Theresa May pledges millions of pounds of government funding to develop artificial intelligence in the healthcare sector to help detect illnesses.
