Government invests £100 million in AI to improve healthcare
The UK government has pledged £100 million to accelerate AI related technologies to help address the most serious health issues in Britain.
7 minute readArtificial intelligence, or AI, is rarely out of the news and is a topic that often stirs up strong emotions.
On the one hand, AI has the potential to make our lives easier; an essay at the touch of a button, self-driving cars, a virtual assistant on hand 24 hours a day. However, many of us also fear the power of AI. Is it really smarter than us? Are our jobs at risk? And what if robots decide to take over the world?
One of the few areas of AI where opinions are less divided is healthcare. This week, the UK government pledged £100 million to accelerate AI related technologies to tackle the most serious health issues.
What is the new mission to invest in AI?
The Departments for Health and Social Care and Science, Innovation and Technology have proposed a new government initiative to accelerate AI-led advances in healthcare. In a speech at the Royal Society of London on October 26th, the Prime Minister outlined plans for the AI Life Sciences Accelerator Mission.
Prime Minister Rishi Sunak said:
“AI can help us solve some of the greatest social challenges of our time. AI could help find novel dementia treatments or develop vaccines for cancer.”
“That’s why today we’re investing a further £100 million to accelerate the use of AI on the most transformational breakthroughs in treatments for previously incurable diseases.”
Michelle Donelan, secretary of state for science, innovation, and technology, commented:
“Safe, responsible AI will change the game for what it’s possible to do in healthcare, closing the gap between the discovery and application of innovative new therapies, diagnostic tools, and ways of working that will give clinicians more time with their patients.”
The new mission, which is subject to a full business case, involves investing £100 million across areas of healthcare where artificial intelligence is deemed to have the greatest potential to tackle previously incurable diseases.
What areas of healthcare does it cover?
The Life Sciences Vision includes 8 critical healthcare missions, including treatment for mental health conditions, dementia, and cancer. The proposal involves collaboration between the government, the NHS, industry, academia, and medical research charities.
The funding will continue for 18 months and will involve the testing and trialling of cutting-edge technologies in areas with the most urgent clinical needs. There is also a 5-year mission to improve mental health research, aiming to improve the lives of those struggling with mental illness.
How could AI improve research, diagnosis, and treatment of these health conditions?
Cancer
In recent years, advancements in AI have been used to make diagnosing cancer faster and more accurate, allowing for earlier diagnosis and treatment that could save lives.
Dementia
The new government funding could allow AI to develop data that quickly identifies those at risk of dementia and related conditions. It can also ensure the right patients are participating in the right trials at the right time. This will allow the development of new, effective treatments for dementia and allow closer monitoring of how well these treatments work.
Mental health treatment
AI can improve both diagnosis and treatment for mental health conditions.
An example of this is conversational AI, a chat bot available 24/7 that engages with patients in a friendly manner and guides them through the referral process. AI also has the ability to screen patients, allowing them to be re-directed to a more appropriate service if needed, and alerting caregivers to high-risk patients who need to be prioritised. Conversational AI also relieves pressure on the NHS and frees up healthcare professionals from time spent on initial data collection.
Is AI currently being used in healthcare settings?
AI is already being used in many areas of healthcare, including research, diagnosis, and treatment.
Administrative applications
The use of AI in healthcare administrative applications includes clinical documentation, medical records management, chatbots for patient interaction, and online doctors.
Machine learning
Machine learning is a technique where machines are able to “learn” by training models with data. In healthcare, machine learning is most commonly used in precision medicine, a way of predicting what treatments are likely to be successful based on a range of data.
A more complex form of machine learning is the neural network, a type of AI used for categorisation applications like determining whether a patient will acquire a particular disease.
Deep learning is the most complex type of machine learning and can be used to recognise potentially cancerous lesions in radiology images. This type of AI has the potential to allow for greater accuracy in diagnosing cancer in the early stages of the disease.
Natural language processing (NLP)
Natural language processing includes applications such as speech recognition, translation, and text analysis.
In healthcare, NLP is predominantly used in the creation, understanding and classification of clinical documentation and published research. This includes the analysis of clinical notes, transcribing patient interactions, preparing reports, and conversational AI.
Robots
Surgical robots offer powerful assistance to surgeons, allowing them to see in more detail and perform tasks such as making precise surgical incisions, and stitching wounds. Common surgical procedures using robotic surgery include prostate, kidney, gallbladder, and gynaecological procedures.
Robotic process automation
This type of AI is involved in performing digital tasks for administrative purposes. In healthcare, this technology is used for tasks like updating patient records or billing.
Rule-based expert systems
Rule based expert systems were the dominant form of AI in the 1980s and follow an “if-then” algorithm. In healthcare, they were used in areas such as clinical decision support and electronic health records.
Rule based expert systems work well at a simple level but tend to be less effective when there are many rules that may conflict with each other. This type of AI is gradually being replaced by other techniques like machine learning.
Diagnosis and treatment applications
Perhaps the most challenging task for AI is the diagnosis and treatment of medical conditions and barely a week goes by without a new “breakthrough” being heralded in the media. An example of this is IBM’s AI programme, “Watson.” Marketed as being able to detect and recommend treatments for cancer, early excitement faded as the technology turned out not to be as reliable or accurate as first thought.
AI is being used increasingly in research labs for tasks such as radiological image analysis, retinal scanning, and genomic-based precision medicine.
Tech giant Google is currently collaborating with health delivery networks, using data to create a prediction model that could warn clinicians of high-risk conditions.
Several firms, such as Foundation Medicine and Flatiron Health, use genetic profiles to recommend treatment for certain cancers.
Another AI-driven technology is “Population Health” machine learning, which aims to predict populations at risk of a particular disease or accident, as well as hospital readmissions.
Patient engagement and adherence applications
Getting patients to engage with and adhere to treatment plans has long been a problem in healthcare. In general, the more proactive and involved patients are in their own care, the better the outcome for both patients and healthcare providers.
Some AI-based apps use personalised, informative content to provide message alerts and targeted, relevant information to encourage and reward behaviours beneficial to health, like diet and exercise.
In addition, information provided by biosensors, smart watches, smartphones, and conversational interfaces allows AI based software to make recommendations for other effective treatments by comparing patient data to that of similar groups.
The future of AI in healthcare
With the rapid growth in AI-related technology causing mixed feelings in many of us, the use of AI in healthcare is largely viewed as positive and exciting. The new government proposal of £100 million to fund the development of AI in healthcare has the potential to create new, innovative, and possibly lifesaving treatments for millions of people in the UK.
