UKRI Centre for Doctoral Training in Artificial Intelligence for Healthcare

Lead Research Organisation: Imperial College London
Department Name: Dept of Computing


The UKRI CDT in Artificial Intelligence (AI) for Healthcare will be the world's leading centre for PhD training of the next-generation innovators in AI applied to Healthcare. There is a unique role for AI in healthcare by providing more accurate decisions faster while reducing cost and suffering across society. AI in healthcare needs and drives current AI research avenues such as interpretable AI, privacy-preserving learning, trust in AI, data-efficient learning and safety in autonomy. These are key due to the immediate impact on life and health for users depending on AI for healthcare support. Healthcare applications require many AI specialists that can apply their skills in this heavily regulated domain. To address this need, we propose to train in total 90+ PhD students including 16 clinical PhD Fellows in five cohorts of 18+ PhDs, which will establish a new generation of cognitively diverse AI researchers with backgrounds ranging from computer science, psychology to design engineering and clinical medicine.
The CDT focus areas arise from our early engagement in AI research and collaboration with clinicians, partnered technology companies and patient organisations, reflecting the healthcare areas of the UK industrial strategy. The Centre is grouped into 4 complementary healthcare themes and 4 cross-cutting AI expertise streams. The 4 healthcare themes are:
(1) Productivity in Care: making healthcare provision more efficient and effective by increasing the productivity of doctors and nurses;
(2) Diagnostics & Monitoring: developing AI-based diagnostics & monitoring that can detect disease earlier and monitor health with more precision;
(3) Decision support systems: AI-based decision support systems that will support e.g. freeing up doctors' time to focus on the patient or can accelerate the development of novels drugs and treatments and empowering patients to be active agents within the decision-making by explaining, and
(4) Biomedical discovery: driven by AI that accelerates drug discovery and linking genome, microbiome and environment data to discover novel disease mechanisms and treatment pathways.
The themes are linked by 4 cross-cutting AI expertise streams:
a. Perceptual AI technology enables to perceive, structure, and recognise from sensory data clinically relevant information.
b. Cognitive AI technology mimics the reasoning, i.e. cognitive process, of healthcare specialists.
c. Assistive AI technology supports clinicians with decision making as well as patients directly
d. Underpinning AI technologies are driving factors for clinical and patient-focused AI innovations and will be enabling AI methodologies to operate beyond the currently possible.

Our unique cohorts will benefit from an integrated training program and co-creation process with industry and patient organisations. PhD training is split into three phases that provide underpinning skill training (Foundation phase), research training (Research Phase) and finally drive PhD impact (Impact phase). During the Impact phase, the students will either
(1) commercialising their research through a mentored start-up route (incubator partners),
(2) deploying their technology in a clinical trial (two NIHR biomedical research centre (BRC) partners), or
(3) testing their work in person through an NHS honorary contract (three NHS trusts as partners).

Bespoke training will be created, such as AI bias & ethics, security, trust, inclusivity, differential privacy, transparency, accessibility and usability, service design, global inclusivity, healthcare treatments, clinical statistics and data regulation, Healthcare technology regulation, and technology commercialisation.

We offer an exit Strategy (month 9-12) through a master's degree. The centre will place special emphasis on research that explores diversity in AI for healthcare research, including services to underserved communities and minority-specific care requirements.

Planned Impact

AI health services and businesses will be essential to reduce the UK's (and global) strain on health care systems. Both industry studies and government white papers unanimously concur that the UK is facing a serious skills gap in Artificial Intelligence. Missing skills are rooted in gaps of training, which threatens the global leadership of the UK AI landscape. The number of roles in AI has risen by 485% in the UK since 2014 (PWC, AI Report, March 2017), many of these require PhD level qualifications due to the complexity of adapting and translating rapidly evolving research into deployable clinical applications. This need is thwarted by an AI skills gap, which has been identified as the single largest factor impeding AI adoption (ibid; Gardner Group, AI Report, 2017) leading the UK government to explicitly recommended a drastic increase in AI PhD training (Growing the AI industry report). The unique opportunities brought by the rapid digitalisation of healthcare implies that not only millions of lives in the UK can be saved and quality of life improved with the help of AI systems, but also significant healthcare provision savings can be achieved (29.4b£ according to Accenture, 2017). This requires AI researchers that can apply their knowledge in the heavily regulated healthcare domain, which has historically high entry barriers for non-clinicians.

We therefore target explicitly the 4 areas highlighted in the industrial strategy on three primary impact areas for AI spanning the healthcare domain:
1. Productivity in Healthcare (e.g. smart hospitals, smart public health monitoring),
2. Diagnostics & monitoring (e.g. AI for imaging, empowering patients to be active agents within the decision-making by explaining),
3. Decision support systems (e.g. automatic personalisation of treatments),
4. Biomedical Discovery (e.g. for accelerating and lowering the cost of drug discovery).

Our Centre is designed around co-creation with industry and clinicians, which will ensure impact by providing three routes to success: graduates may choose to include found a start-up with incubator partners, validate their research during a clinical trials with our NIHR BRC partners, or deploy AI technology with the NHS, through our 3 NHS trust partners.

We will engage with stakeholders from
(1) the clinical practice,
(2) industry,
(3) public policy bodies,
(4) patients and
(5) the public
to ensure the individual research projects and underpinning methodology addresses their need, and to identify commercial and non-commercial exploitation opportunities.

Economic and societal impacts include
(1) training AI researchers in inter-disciplinary research who will be equipped with the skills to support the needs of academia, industry and wider society;
(2) training clinicians in AI applications;
(3) faster, better, safer, cheaper healthcare by improving diagnostics, productivity in care, decision support and drug discovery with commercialization by spin-outs or established companies;
(3) improving the quality of life by enhancing healthcare technology available to support clinical decision-making.

Impact on knowledge will include the realization of new approaches applying AI technologies in Healthcare. The research conducted will result in healthcare, scientific and engineering innovations.
We expect that the proposed UKRI CDT will lead to
(1) 90+ PhD graduates including clinicians with a unique expertise in AI technology to fill the current AI skills gap in the UK economy and sciences.
(2) a high number of patents and technology licenses and numerous founded start-ups
(3) research outputs that will lead to follow-on funding
(4) further commercialization partners for individual research projects and a growing network beyond a large number of partners who support the centre already at this stage.
(5) A high volume of high profile scientific publications, references in media, and public engagement activitie


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023283/1 01/04/2019 30/09/2027
2281170 Studentship EP/S023283/1 28/09/2019 27/12/2023 Rakhilya Lee Mekhtieva
2281176 Studentship EP/S023283/1 01/10/2019 30/11/2023 Ruby Sedgwick
2284317 Studentship EP/S023283/1 01/10/2019 02/06/2023 Kavitha Vimalesvaran
2281175 Studentship EP/S023283/1 01/10/2019 30/09/2023 Margherita Rosnati
2281165 Studentship EP/S023283/1 01/10/2019 31/01/2024 Digby Chappell
2281173 Studentship EP/S023283/1 01/10/2019 12/02/2024 Benjamin Post
2281177 Studentship EP/S023283/1 01/10/2019 30/11/2023 Reneira Seeamber
2281159 Studentship EP/S023283/1 01/10/2019 31/07/2020 Garazi Arana Oiarbide
2281183 Studentship EP/S023283/1 01/10/2019 31/12/2022 Sameer Zaman
2281179 Studentship EP/S023283/1 01/10/2019 30/09/2023 Alina-Irina Serban
2281152 Studentship EP/S023283/1 01/10/2019 30/09/2023 James Batten
2281169 Studentship EP/S023283/1 01/10/2019 30/09/2023 Annalaura Lerede
2366544 Studentship EP/S023283/1 06/11/2019 05/11/2023 Sumeet Hindocha
2455000 Studentship EP/S023283/1 05/08/2020 04/08/2024 Myura Nagendran
2446483 Studentship EP/S023283/1 05/10/2020 04/10/2024 Berke Basaran
2446482 Studentship EP/S023283/1 05/10/2020 04/10/2024 Nur Aizaan Binti Anwar
2454594 Studentship EP/S023283/1 05/10/2020 04/10/2024 Neophytos Polydorou
2447628 Studentship EP/S023283/1 05/10/2020 04/10/2024 Stefano Falini
2447664 Studentship EP/S023283/1 05/10/2020 04/10/2024 Adam Marcus
2447638 Studentship EP/S023283/1 05/10/2020 04/10/2024 Christoforos Galazis
2454840 Studentship EP/S023283/1 05/10/2020 04/10/2024 Blanka Zicher
2454646 Studentship EP/S023283/1 05/10/2020 04/10/2024 Hadrien Reynaud
2446490 Studentship EP/S023283/1 05/10/2020 04/10/2024 William Bolton
2447667 Studentship EP/S023283/1 05/10/2020 04/10/2024 Amr Nimer
2454763 Studentship EP/S023283/1 05/10/2020 04/10/2024 Joshua Southern
2447629 Studentship EP/S023283/1 05/10/2020 04/10/2024 Paul Festor
2454811 Studentship EP/S023283/1 05/10/2020 04/10/2024 Michael Thornton
2449184 Studentship EP/S023283/1 05/10/2020 04/10/2024 Edoardo Occhipinti
2447625 Studentship EP/S023283/1 05/10/2020 04/10/2024 Ariane Duverdier
2447402 Studentship EP/S023283/1 05/10/2020 04/10/2024 William Dudley
2454822 Studentship EP/S023283/1 05/10/2020 04/10/2024 Dekai (Kai) Zhang
2454771 Studentship EP/S023283/1 05/10/2020 04/10/2024 Michael Tanzer
2447401 Studentship EP/S023283/1 05/10/2020 04/10/2024 Xavier Cadet
2451827 Studentship EP/S023283/1 05/10/2020 04/10/2024 William Plumb
2446481 Studentship EP/S023283/1 05/10/2020 04/10/2024 Asem Mohamed Alaaeldin Abdelaziz
2447661 Studentship EP/S023283/1 05/10/2020 04/10/2024 Megan Hutchings
2447641 Studentship EP/S023283/1 05/10/2020 04/10/2024 Annika Guez
2437590 Studentship EP/S023283/1 05/10/2020 04/10/2024 Ioannis Afentakis
2481146 Studentship EP/S023283/1 04/01/2021 03/01/2025 Alistair Weld
2481144 Studentship EP/S023283/1 04/01/2021 03/01/2025 Agnese Grison