UKRI Centre for Doctoral Training in Artificial Intelligence for Medical Diagnosis and Care

Lead Research Organisation: University of Leeds
Department Name: Sch of Computing


Artificial Intelligence (AI) has advanced rapidly over the last five years, largely as a result of new algorithms, affordable hardware, and huge increases in the availability of data in digital form.

The UK has recognised as a national priority the urgent need to exploit AI in human health, where digital data is being created from many sources, for example: images from tissue slices, X-ray devices, and ultrasound; along with laboratory tests, genetic profiles, and the health records used by GPs and hospitals.

The potential is enormous. In future, AI could automatically identify those at risk of cancer before symptoms appear, suggesting changes in lifestyle that would reduce long-term risk. It could greatly speed-up and increase the reliability of diagnostic services such as pathology and radiology. It could help doctors and patients select the most appropriate care pathway based on personal history and clinical need.

Such improvements will lead to better care and more cost-effective use of resources in the NHS.

Our Centre for Doctoral Training will train the future researchers who will lead on this transformation. They will come from a variety of backgrounds in science, engineering and health disciplines. When they graduate from the Centre after four years, they will have the AI knowledge and skills, coupled with real-world experience in the health sector, to unlock the immense potential of AI within the health domain.

Our scope is on AI for medical diagnosis and care with a focus on cancer for which there are particularly rich sources of digital data, and where AI is expected to lead to significant breakthroughs. Leading with cancer, we will inform the use of AI in medical diagnosis and care more widely.

The Centre will be based in the City of Leeds, which has developed into the home of the NHS in England. The University of Leeds and the Leeds Teaching Hospitals Trust (LTHT), working with key national partners from the NHS and industry, provides the ideal environment for this Centre. There is internationally excellent research on AI and on cancer, including a world leading centre for digital pathology. There is already strong collaboration between the different organisations involved.

The Centre builds on a well-established track record in transferring research ideas into world-leading clinical practice and new products. Our graduates will become international leaders in academia and industry, ensuring the UK remains at the forefront in health research, clinical practice and commercial innovation.

Planned Impact

This CDT is driven by national priorities, urgent demands, and opportunities brought by data digitalisation.

The benefits are:


These are the people who join the CDT to receive a research training in the application and implications of AI to Medical Diagnosis and Care. They will be strongly positioned to enter an exciting and rewarding career path in at least three main areas:

(1) As academic members of staff in a University, with good research funding prospects and high potential societal and economic impact from research outputs. They may join a STEM or health-related disciplines.

(2) As entrepreneurs, developing a business idea based on the application of AI within the health domain.

(3) As members of a commercial company (e.g. TPP) or public sector organization (e.g. NHS Digital), working on the origination and development of innovative new products.


The CDT will fulfil an urgent need for training a critical mass of researchers with the multidisciplinary knowledge and skills necessary to realise the huge potential for AI to transform clinical practice in medical diagnosis and care. The research conducted within the CDT will itself also contribute a benefit to society.

Examples of the benefits for patients will be in:

(1) Improving survival rates and care costs from serious conditions such as cancer.

(2) Identifying those at high risk of a delayed diagnosis of cancer and generating evidence to target contextualized interventions.

(3) Identifying those at low risk of having cancer from basic demographic, clinical and biochemical data, and developing pathways of care that deliver a stratified response.

(4) Identifying optimal patient pathways, ensuring prompt diagnosis referrals.

(5) Analysing images automatically within digital pathology, digital radiology and digital photography (e.g. pigmented skin lesions) to accelerate and improve accurate diagnosis.

(6) Risk-stratifying patients into those who would benefit from major surgery or those where minor interventions would be a better option.

(7) Empowering health professionals to provide more personalised patient-centred care.

(8) Giving patients intelligent tools to find their own best clinical pathways, given their understanding of the risks and life-trade-offs.


The economic impact of the CDT will be in:

(1) Financial savings through more rapid and accurate diagnosis and better targeted treatment.

(2) Creation of new commercial opportunities arising from the innovations developed by CDT researchers during their time in the CDT and in their subsequent careers. In diagnostic devices, software tools for interpretation of diagnostic data, clinical information systems, distributed systems (e.g. sensor networks). A distinctive focus of the CDT is on multi-modal integration of AI systems. There is immense scope here for commercial development of systems that combine modalities to discover new information that is not present in a single source, ranging over video (e.g. from ultrasound), images (histopathology, medical photographs), three-dimensional scans and models, genomics and molecular data, text and coded health records.

(3) Increase in national productivity through decreasing hours lost for medical reasons.


The impact of the CDT for the global knowledge base will be in outstanding academic outputs from researchers with the advantage of:

(1) Bridging the gap between the very different but highly synergistic areas of AI and health.

(2) Being grounded in the highly promising and expanding areas of multi-modal integration, and AI systems that can explain their behaviour.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S024336/1 01/04/2019 30/09/2027
2271711 Studentship EP/S024336/1 01/10/2019 30/09/2023 Samuel Llanwarne
2267727 Studentship EP/S024336/1 01/10/2019 30/09/2023 Lucy Olivia Godson
2271791 Studentship EP/S024336/1 01/10/2019 30/09/2023 Joe Sims
2267128 Studentship EP/S024336/1 01/10/2019 30/09/2023 Andrew Broad
2271663 Studentship EP/S024336/1 01/10/2019 30/09/2023 Anna-Grace Linton
2271095 Studentship EP/S024336/1 01/10/2019 30/09/2023 Jason James Keighley
2276519 Studentship EP/S024336/1 01/10/2019 30/09/2023 Rachel Harkness
2271306 Studentship EP/S024336/1 01/10/2019 30/09/2023 Sara Angharad Jones
2267095 Studentship EP/S024336/1 01/10/2019 30/09/2023 Thomas Allcock
2440313 Studentship EP/S024336/1 01/10/2020 30/09/2024 Mary Louisa Paterson
2440761 Studentship EP/S024336/1 01/10/2020 30/09/2024 Jack Joseph Breen
2440852 Studentship EP/S024336/1 01/10/2020 30/09/2024 Sarah Leanne Miller
2437081 Studentship EP/S024336/1 01/10/2020 30/09/2024 Alexander David Coles
2440868 Studentship EP/S024336/1 01/10/2020 05/03/2021 Jonathan David West
2440847 Studentship EP/S024336/1 01/10/2020 30/09/2024 Shazeea Masud
2440255 Studentship EP/S024336/1 01/10/2020 30/09/2024 Zoe Louise Hancox
2437072 Studentship EP/S024336/1 01/10/2020 30/09/2024 Sian Carey
2440860 Studentship EP/S024336/1 01/10/2020 30/09/2024 Benjamin Isaac Wilson
2440887 Studentship EP/S024336/1 01/10/2020 30/09/2024 Emma Lucy Briggs
2440347 Studentship EP/S024336/1 01/10/2020 30/09/2024 Sarah Louise Smith