Today, August 10, the Secretary of State for Science and Technology, Michelle Donelan, announced 22 projects that will explore how to develop and use AI in health.
The announcement comes on the same day that the government appointed two leading experts to spearhead preparations for the UK to host the first major international summit on the safe use of AI.
Matt Clifford and Jonathan Black will be charged with rallying leading AI nations, companies and experts, ahead of the event in the UK this autumn.
Matt is the CEO of Entrepreneur First and Chair of the Advanced Research and Invention Agency. Jonathan is a Heywood Fellow at the Blavatnik School of Government at the University of Oxford and former UK G7 and G20 Sherpa and Deputy National Security Adviser.
Funding from the £250m UKRI Technology Missions Fund
The projects will involve universities stretching from Edinburgh to Surrey.
They will be supported by £13 million from UK Research and Innovation’s (UKRI) Technology Missions Fund, previously announced in the Science and Technology Framework, to support AI innovation to accelerate health research.
Unveiling projects during visit to UCL
This includes more than £500,000 for University College London’s (UCL) Centre for Interventional and Surgical Sciences.
The Secretary of State visited it yesterday to observe the early development of technology that could revolutionise surgery for one of the most common types of brain tumour.
The project will develop a real-time AI ‘assisted decision support framework’ to improve surgical outcomes, including avoiding complications following surgery and shortening recovery time for patients.
Other funded projects include those led by:
- University of Sheffield: £463,000 to carry out an external validation of an approach that could lead to much wider, effective treatment of chronic nerve pain. This affects one in 10 adults over 30 to better detect early signs of inflammatory arthritis that could mean earlier, more effective treatment
- University of Oxford: £640,000 to accelerate research into a foundation AI model for clinical risk prediction that could determine the likelihood of future health problems based on an individual’s existing conditions
- Heriot-Watt University in Edinburgh: £644,000 to develop a system that assists trainee surgeons to practice laparoscopy procedures, commonly known as keyhole surgery, with real-time feedback on their movements
- University of Surrey: £456,000 will see them work closely with radiologists to develop AI that improves the mammogram analysis process. This could allow radiologists to join the clinical force earlier in their careers, boosting the numbers of cancer specialists
Home of safe innovation
Technology Secretary Donelan said:
The UK has a proud history of demonstrating diplomatic leadership on the most important issues of the day and Matt and Jonathan’s experience and expertise means that they are perfectly placed to lay the groundwork ahead of talks this year on safe and responsible AI.
We’re already a leading nation when it comes to artificial intelligence – and this summit will help cement our position as the home of safe innovation.
By leading on the international stage, we will improve lives at home. AI will revolutionise the way we live, including our healthcare system. That’s why we’re backing the UK’s fantastic innovators to save lives by boosting the frontline of our NHS and tackling the major health challenges of our time.
Improving health diagnostics and outcomes
Dr Kedar Pandya, Executive Director, Cross-Council Programmes at UKRI, said:
The potential for AI to accelerate and improve all aspects of our health is vast.
The UK is in a strong position in this field but with a range of challenges across many aspects of society, including the healthcare system, novel solutions are needed. That is why UKRI is investing in these projects in order to advance our research and improve health diagnostics and outcomes.
Full list of projects
Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis
Professor Weizi Li, University of Reading
The project uses AI to improve detection and personalised disease outcome prediction of inflammatory arthritis.
INDICATE: AI-enabled data curation, quality and fact-checking for medical documents
Dr James Kinross, Imperial College London
The project uses AI for real time analysis of healthcare infodemics to autonomously create clinical guidance and identify misinformation.
Autonomous and intelligent laparoscopy trainer with real-time feedback
Dr Mustafa Suphi Erden, Heriot-Watt University
The project is developing an AI-based self-training platform for laparoscopic surgery.
AI for personalised respiratory health and pollution (AI-Respire)
Professor Kian Fan Chung, Imperial College London
The project uses AI to develop a predictive tool for the impact of pollution on health.
Privacy-preserving artificial intelligence for fibrosis progression prediction for patients with neovascular AMD
Professor Daniel Rueckert, Imperial College London
The project is developing privacy-preserving AI for the diagnosis and treatment of age-related macular degeneration.
Clinical prediction foundation models for individuals with multiple long-term conditions
Professor Christopher Yau, University of Oxford
The project uses foundation AI models to develop clinical risk prediction models for multiple long-term conditions.
Artificial intelligence methods applied to genomic data for improved health (AGENDA)
Professor Sarah Ennis, University of Southampton
The project uses AI to determine clinical impact of newly discovered variations in the genome.
From 2 million to 20 million: scaling and validating a foundation model for ophthalmology
Dr Pearse Keane, UCL
The project uses foundation AI models for ophthalmic imaging and diagnosing retinal diseases.
AI-based diagnosis for improving classification of bone and soft tissue tumours across the UK
Dr Charles-Antoine Collins-Fekete, UCL
The project uses AI to develop novel cancer diagnosis techniques using soft tissue tumours as a use case
RELOAD: REspiratory disease progression through LOngitudinal Audio Data machine learning
Professor Cecilia Mascolo, University of Cambridge
The project uses AI to analyse breathing and speech to identify patient at risk of developing severe respiratory tract infections.
Optimisation of natural language processing for real-time structured clinical data capture in electronic health records
Dr Anoop Shah, UCL
The project uses AI to improve the recording of structured clinical data at the point of care.
AI-enabled targeting of public health interventions through dynamic characterisation of the environment
Dr Soren Brage, University of Cambridge
The project uses AI to determine how location and environment factors affect behaviours towards diet and physical activity.
AID-PitSurg: AI-enabled Decision support in Pituitary Surgery to reduce complications
Professor Dr Sophia Bano, UCL
The project is developing an AI-assisted decision support framework to improve surgical outcomes.
Artificial Intelligence for Pollen and Spore Detection, Forecasting and Human Health (AIPS)
Professor Francis Pope, University of Birmingham
The project combines IoT sensors and AI to detect and forecast allergenic and toxic bioaerosols.
A novel artificial intelligence powered neuroimaging biomarker for chronic pain
Dr Dinesh Selvarajah, The University of Sheffield
The project uses AI to identify new biomarkers and developing therapeutics for neuropathic pain.
Infection-AID: AI assisted genomic profiling to inform the diagnosis, personalised treatment and control of infections
Professor Taane Clark, London School of Hygiene and Tropic Medicine
The project uses AI to reveal drug resistance mutations and transmission patterns in infectious diseases.
Leveraging universal fractal geometry to develop new AI for neuroimaging
Dr Yujiang Wang, Newcastle University
The project develops novel AI-based non-invasive neuroimaging.
Self-learning AI-based digital twins for accelerating clinical care in respiratory emergency admissions (SLAIDER)
Professor Lu Lui, University of Leicester
The project develops a digital twin for enhancing clinical care in respiratory emergency conditions.
Efficient AI tools for equitable handling of missing values in population-wide e-health records to advance prevention of chronic diseases
Dr Angela Wood, University of Cambridge
The project uses AI to handle missing data in electronic health records multiple long-term conditions risk prediction.
Sample Size guidance for developing and validating reliable and fair AI PREDICTion models in healthcare (SS-PREDICT)
Professor Richard Riley, University of Birmingham
The project develops new guidance and methods to generate reliable and fair AI prediction models for use in healthcare.
People-centered mammogram analysis
Professor Gustavo Carneiro, University of Surrey
The project is developing AI models for mammogram analysis with focus on co-creation with radiologists.
IDERT: Intelligent Deimmunization for Enzyme Replacement Therapies
Dr Giovanni Stracquadanio, The University of Edinburgh
The project uses AI to design new therapies for rare inherited diseases.
Top image: Credit: Department for Science, Innovation and Technology