City Focus: London

Georgia Wilson
- Leadership - Oct 04, 2019

Business Chief Europe looks at London’s tech world and how the NHS is implementing innovative AI technology in healthcare


London, “A unique blend of talent, investment and innovation” – London and Partners. In 2018, digital technology saw a turnover of £64.11bn ($US78.24bn), employed 263,000 people and ranked highest in the UK for investors in technology. With 51,690 digital tech businesses and a growth rate of 56% for tech startups, London is one of the world’s leading technology hubs.

In the last 10 years alone, technological advancements have been exponential. Technology we couldn’t live without today such as the Cloud, chatbots, online streaming and Uber didn’t exist. Even GPS on phones wasn’t widely available back in 2009.

Today, technology continues to advance at an intense rate, in particular artificial intelligence (AI). Such advancements include the cloud, practical augmented reality, machine learning, AI robotic bars and self-driving cars.

In addition, AI as an industry has also advanced significantly. As of 2018, London has four AI universities in the world’s top 40, 645 AI company headquarters and has received the highest VC funding in Europe for AI of £1.8bn ($US2.2bn) all of which continue to strengthen the city’s strong global position for AI.

Looking into specific industries, a key sector that has seen a widespread of innovative AI technology introduced into its field in the last couple of years is healthcare – in particular, the NHS. With a vision of being a world leader in AI and machine learning within five years, the NHS is fully committed to innovative development of AI.

Recent NHS trials for AI technology

In August 2019, the NHS announced a £250mn ($US305mn) government investment to create a national AI laboratory focused on enhancing patient care and research, specifically for cancer, dementia and heart disease.

Other recent trails carried out by the NHS include collaboration with London based organisations such as: University College London Hospitals (UCLH), Microsoft, HeartFlow and King’s College London.

University College London Hospitals (UCLH)

As part of a broader project to bring the benefits of AI to the NHS, UCLH has produced an algorithm using records from 22,000 appointments for MRI scans. The algorithm successfully identified 90% of the people who are most likely not to attend their appointment. “On average we estimate this could save £2-3 per appointment,” says National Hospital for Neurology and Neurosurgery (NHNN) consultant, Parashkev Nachev.

Current health secretary, Matt Hancock commented on the software, “Missed hospital appointments waste patient and staff time, prevent sick people from being seen at the earliest opportunity and cost our amazing NHS an unjustifiable amount of money.” He added, “Artificial intelligence has enormous potential to revolutionise healthcare and this is exactly the type of innovation our NHS needs to embrace to ensure every penny goes further as part of the long-term Plan.”


In 2018, Microsoft, Cambridge University and Addenbrooke’s Hospital reported that their machine learning software developed to automate delineation of tumours has been successfully streamlining the previously multiple hour-long process of outlining borders and contours of prostate cancers. The project, known as Project InnerEye, started development back in 2016.

“This collaboration between the InnerEye team at Microsoft and the Department of Oncology at Addenbrooke’s is an example of the type of innovation that we wish to promote within the NHS. It is a good example of what can be achieved when the Trust works in collaboration with industry and the University in order to produce cutting edge technologies with real-world applications in patient care, to benefit the UK,” says Chief Executive Officer, Cambridge University Hospitals NHS Foundation Trust, Roland Sinker.


In April 2018, the NHS adopted the HeartFlow FFRct Analysis. Following a non-invasive coronary CTA, the HeartFlow software uses deep learning to create a personalised, 3D digital model of a patient’s coronary arteries. The software then uses powerful algorithms to assess the impact of blockages and determine how to stimulate blood flow. Trials have demonstrated that the technology can reduce unnecessary and invasive diagnosis and procedures that can lead to high risk complications.

“Using the HeartFlow Analysis has transformed our paradigm for investigating chest pain. It has dramatically reduced the numbers of patients requiring invasive investigation and has allowed strategic targeting of therapy for those patients who still require invasive angiography, which saves both time and expense,” says Interventional Cardiologist, Dr Philip Strike at Queen Alexandra Hospital, Portsmouth. “It has allowed sensible and safe waiting list management and allowed prioritisation of higher risk patients by removing unnecessary invasive assessment in other patients.”

King’s College London

When it comes to cancer diagnosis, 50% of patients are diagnosed in the last stage of growth. As a result, 80% will die within five years. With earlier diagnosis, those 80% could survive for at least five more years.

The NHS began working with King’s College London and C the Signs earlier this year to support its multi-platform tool that allows early identification of patients at risk of cancer. The software uses AI mapped with the latest NICE guidelines and other relevant evidence, allowing GPs to check combinations of signs, symptoms and risk factors to identify which referrals and investigations are required.

The tool has been successfully trailed in Sutton and is now being piloted in Merton and Wandsworth.

Although there are certainly challenges to face as the use of AI in healthcare increases – such as privacy, ensuring data is correct, and potential cyber threats – there are multiple benefits to be seen from the innovative steps made in AI. Benefits include: increasing time efficiency, reducing missed appointments, identifying treatments and referrals so that more time can be spent on severe cases. As well as earlier diagnosis of high-risk illnesses, higher accuracy when outlining tumours and reducing invasive diagnoses and procedures.

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