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Details of Grant 

EPSRC Reference: EP/S021612/1
Title: UKRI Centre for Doctoral Training in AI-enabled healthcare systems
Principal Investigator: Taylor, Professor P
Other Investigators:
Researcher Co-Investigators:
Project Partners:
AstraZeneca AT Medics Ltd Atos Origin IT Services UK Ltd
BenevolentAI Bio Ltd Crystallise Limited DeepMind
Great Ormond Street Hospital IQVIA Moorfields Eye Hosp NHS Foundation Trust
NHS Digital (previously HSCIC) Oracle Cerner Public Health England
Royal Free London NHS Foundation Trust UCL Hospitals NHS Foundation Trust Visulytix Ltd
Whittington Hospital NHS Trust
Department: Office of Vice Provost Research
Organisation: UCL
Scheme: Centre for Doctoral Training
Starts: 01 April 2019 Ends: 30 September 2027 Value (£): 6,719,271
EPSRC Research Topic Classifications:
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Nov 2018 UKRI Centres for Doctoral Training AI Interview Panel V – November 2018 Announced
Summary on Grant Application Form
PhD projects will be organised in three central themes that represent the core of our programme. The themes are aligned to the strategic priorities of our NHS partners and the overall vision of the CDT:

A. AI-enabled diagnostics or prognostics [lead; McKendry]. Deep learning - the subset of machine learning that is based on a network structure loosely inspired by the human brain - enables networks to learn features from clinical data automatically. This gives them the ability to model complex non-linear relationships and such AI methods have found application in clinical diagnosis using either parameters typically embedded in an electronic health record (like blood test results) or the images produced during radiographic exams or in digital pathology suites. This theme will help us create, initiate and deploy academic research projects centred on clinical use cases of direct applicability in the hospitals where our Centre is based. Example projects might include the detection of radiology abnormality; characterisation of tissues and tissue abnormality (e.g. cancer staging); or the serial monitoring of disease.

B. AI-enabled operations [lead; Marshall] The proximity of our Centre to the end-users of health technology prompts a second focus, on the use of AI methods to optimise care processes and pathways. We will ensure that our projects are academically focused, but will seek to create new approaches to investigate and characterise the performance of hospitals systems and processes - such as the flow of patients through emergency departments, AI-enabled projects that might shorten time-to-treatment or cancer waits. This will be the most translationally focused theme, seeking to surface and address key use cases of the greatest academic interest.

C. AI-enabled therapeutics [lead; Denaxas]. Our final theme is forward looking; the use of deep learning and other AI methods in therapeutic inference or even in a therapy itself. AI methods may be most applicable here in mental health, where deployment of 'talking therapies' is as efficacious through the internet or telephony as face-to-face; or in the development of 'avatar therapies' such as that recently proposed at UCL for hallucinations. But a wide variety of research projects are conceivable, including rehabilitation following stroke; or indeed the use of AI monitoring of radiological change as a proxy endpoint for drug trials. This theme will help us focus cutting-edge work in our Centre around such use cases and novel methodology.

The UK leads in the development of artificial intelligence technologies, investing around $850M between 2012-16, the third highest of any country. This has catalysed significant UK involvement of major global technology companies such as Alphabet and Apple, the creation of new UK-based AI companies such as Benevolent AI and DeepMind (both partners in our Centre) and the emergence of a vibrant UK SME community. 80% of AI companies on the UK Top 50 list are based in London, most with 30 minutes travel from UCL. Many of the most successful AI companies now focus on the application of AI in health, but the successful application of AI technologies such as deep learning has three key unmet needs; the identification of clinically relevant use cases, the availability of large quantities of high quality labelled data from NHS patients, and the availability of scientists and software engineers with the requisite algorithmic and programming skills. All three are addressed by our CDT, its novel NHS-embedded approach to training, linked to primary and social care and with close involvement of commercial partners, structured internships and leadership and entrepreneurship. This will create an entirely new cadre of individuals with both clinical knowledge and algorithmic/programming expertise, but also catalyse the creation and discovery of new large labelled datasets and exceptional clinical use cases informed by real-world clinical care.

Key Findings
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