UKRI Centre for Doctoral Training in AI-enabled healthcare systems

Lead Research Organisation: University College London
Department Name: Office of Vice Provost Research

Abstract

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.

Planned Impact

Who might benefit from our research and how?
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We identify a wide range of people, partner organisations, society and the economy as being potential beneficiaries of the research and training conducted in this AI-enabled healthcare CDT. These include:

PEOPLE

1. Our students will benefit from the acquisition of new knowledge, skills and training that will equip them to progress more rapidly in their careers, create new companies and transform global healthcare systems.
2. Patients will benefit from the development of new diagnostics and therapeutics and new approaches to organisational optimisation of healthcare systems. Benefits will be both in terms of improved health, quicker recovery from disease, but also more general wellbeing and personal economic benefits from more rapid and effective delivery of healthcare.
3. Healthcare professionals will benefit from new diagnostics and therapeutics that enhance their working environment and allow them to deliver greater healthcare benefits in primary and secondary care. They may benefit from improved job satisfaction and better staff recruitment and retention into healthcare professions
4. Hospital managers will benefit from better tools to optimise performance of healthcare systems both in terms of health outcomes and in terms of financial optimisation
5. Political and healthcare leaders will benefit from improved satisfaction in the delivery of healthcare systems and health outcomes.

PARTNERS

6. NHS partners will benefit from a more engaged, AI-enabled workforce plus graduates from the programme who will be inspired to work in healthcare settings to deliver transformational applications of AI.
7. Commercial partners will benefit from a pipeline of high quality graduates trained not only in the fundamentals of AI and algorithmic approaches but also have deep knowledge of healthcare as one of the major application areas of the future
8. Commercial partners will benefit from the catalytic effect of our Centre in helping build deeper partnerships of knowledge and trust between NHS providers, clinicians and AI companies working in healthcare.
9. SMEs will benefit from the capacity of our Centre to mobilise advice and expertise that they may not have in-house

SOCIETY

10. The new knowledge we generate at the interface between AI and clinical practice will be of intense interest and benefit to policy makers seeking to develop regulatory frameworks for AI-enabled healthcare, or to address fundamental problems of bioethics or information privacy associated with healthcare data
11. The knowledge generated by our research will lead to new AI-enabled products and procedures that will potentially have direct benefits for health and quality of life of society more generally.
12. The knowledge generated in this CDT will potentially be translatable into resource-poor settings or low and middle income countries, thus benefitting international development.

ECONOMY

13. The wider UK economy will benefit from the development of new diagnostic, therapeutic and operational AI-enabled products and procedures emerging from the research conducted in this CDT.
14. New companies will be created by the students in our CDT, creating both employment and wealth creation as well as encouraging inward investment in the UK economy.
15. Existing companies partnering with our CDT will benefit from being able to create employment and wealth
16. Commercial partners who benefit from the research conducted in our CDT are likely to increase inward investment in the UK.

Our pathways to impacts, described in the Appendix, sets out in detail the mechanisms by which we will create this full range of impacts. In doing this, we will exploit the broad and deep translational, commercial partnership, knowledge exchange, technology transfer and policy engagement resources provided by UCL.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S021612/1 01/04/2019 30/09/2027
2245620 Studentship EP/S021612/1 23/09/2019 30/09/2023 Abigail East
2259024 Studentship EP/S021612/1 23/09/2019 30/09/2023 Andre Vauvelle
2245964 Studentship EP/S021612/1 23/09/2019 30/09/2023 Thilina Nuwan Jayatilleke
2245851 Studentship EP/S021612/1 23/09/2019 30/09/2023 Peter Timothy Woodward-Court
2248041 Studentship EP/S021612/1 01/10/2019 30/09/2023 Anthony Peter Bourached
2254638 Studentship EP/S021612/1 01/10/2019 30/09/2023 Marta Monika Kwiatkowska
2247846 Studentship EP/S021612/1 01/10/2019 30/09/2023 Joseph Farrington
2252409 Studentship EP/S021612/1 01/10/2019 30/09/2023 Dominic Matthew Giles
2247858 Studentship EP/S021612/1 01/10/2019 30/09/2023 Joanna ROSALIND Sheppard
2265357 Studentship EP/S021612/1 01/10/2019 30/09/2023 Roy Schwartz