The UK government knows what it wants as far as the use of artificial intelligence in the NHS is concerned.
In fact, it wants innovation, data and AI to “transform the prevention, early diagnosis and treatment of chronic diseases by 2030”, with the United Kingdom to be “at the forefront of the use of AI and data in early diagnosis, innovation, prevention, and treatment”.
Based on the government’s vision, AIs could eventually be the first line of contact for patients as opposed to a human physician.
The AIs could assist healthcare experts in diagnosing medical conditions, as well as monitoring people’s health by assessing the data derived from their smart-home sensors or wearable devices.
Undoubtedly, there are signs of an increasing need for AI technology in the health service space: almost half of all NHS trusts are currently said to be investing in AI in some way.
So far, the adoption of artificial intelligence has been mainly propelled by the need to meet one of two goals: to boost clinical practice, leveraging AI to assist doctors and other medical experts in conducting their diagnosis or even improving support and administration by using artificial intelligence in streamlining resource allocation and daily office management.
Although leveraging artificial intelligence to make the NHS’s administration and support roles more effective might be less exhilarating, it is essential.
AI can be utilized in handling routine administration tasks and ensuring more efficient resource scheduling.
This may help save money as opposed to patients’ lives.
For example, a given trail is utilizing AI-based chatbots as the first point of contact for patients calling the NHS instead of being linked with a human being.
Due to the increasing demands on the NHS’s resources and its underfunding, such applications of artificial intelligence (AI) have the possibility of making the most significant short-term contribution, especially to the NHS.
On the other side, several trial initiatives utilize some elements of machine learning and artificial intelligence.
Some of the NHS’s highly visible AI uses in this particular field are those involving its partnership with Google’s AI arm DeepMind.
DeepMind has joined forces with several institutions including Moorfields Eye Hospital and the University College London Hospitals trust to boost its progress.
When it comes to training AI, the NHS appears to have a considerable advantage.
As the world’s largest healthcare organization, it boasts more than 1.7 million staff and sees about one million patients after every 36 hours.
This situation means that the organization is rich in data, which can be used in AI training.
“The UK ranks fourth in the world when it comes to creating the right conditions for a home-grown AI industry to flourish. The decades of big data held by the NHS represents perhaps the UK’s single biggest opportunity to advance that position and develop world-beating applications. We see huge opportunities around healthcare outcomes and productivity,” wrote Shamus Rae, a KPMG partner and head of digital disruption.
Thanks to its massive store of data spanning from healthcare experts to patients, you’d easily think that the NHS’s datasets would be diverse and big enough to make them virtually perfect for training AI algorithms.
Contrary to your imagination, the NHS’s healthcare trusts do not all use the same labeling technique for their data or even similar IT infrastructure.
For this reason, the organization’s datasets are not only limited but also harder to integrate and share than what you’d expect.
“An AI system is only going to be as good as the data it is drawing upon to learn from, and as we understand it, the NHS does not collect data in a systematic way… I think there’s this idea that the NHS is this amazing resource of data because we have this nationwide healthcare system, but we’re probably quite a long way off having a nationwide set of data that AI could use,” Catherine Joynson, the deputy director at Nuffield Council for Bioethics, an independent body that scrutinizes and compiles reports on ethical issues in medicine and biology.
Additionally, patient notes are still paper-based in all areas of care (both primary and secondary), which means that a massive digitization initiative ought to be executed before this information can be converted into a useful resource for AIs.
Artificial intelligence might assist in fixing this issue; a given project looks forward to using machine learning in sifting through the digitized versions of the massive heaps of document in a bid to locate the information that doctors require, saving them a lot of time that can be used to focus on patients instead of going through old notes.
Even though the NHS can achieve tremendous things with the help of AI, it first needs to deal with the IT basics.
Think-tank Reform recently revealed a report focusing on NHS’s AI use.
The report said that the NHS has to boost the interoperability of all its existing IT systems and proceed with its digital plans in a bid to ensure that they stick to open standards in the future.
“AI is not the panacea for these back-end implementation challenges, and it will not be possible to reap the benefits of this technology at scale if these barriers are not overcome,” warned think-tank Reform.
AI faces its own challenges, which have to be overcome before the healthcare organization can move forward with its AI adoption plans.
With medical records being among the most valuable personal data available today, how the NHS deals with its patient data in this AI age is crucial. “Like any other technology we’ve seen in the NHS, there’s some hype, and there’s also some scepticism. With the case of AI, there’s a little more of an issue when it comes to showing confidence and trust in the technology. Achieving public trust is of vital importance for the success of AI in the NHS,” said Dr Panos Constantinides, the academic director of the AI Innovation Network at the Warwick Business School and associate professor of digital innovation.
“When there is a leak somewhere,when the data is not managed appropriately, when there is not consent, people start asking questions, and that hurts the benefits of the technology — and the technology can do a lot,” added Constantinides.