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UKRI AI Centre for Doctoral Training in Digital Healthcare

Lead Research Organisation: Imperial College London
Department Name: Computing

Abstract

The UKRI AI CDT in Digital Healthcare (DigitalHealth CDT) will build on our established track record of research training to create a world-leading centre for PhD training of the next-generation innovators in AI applied to Digital Healthcare. Using AI in healthcare will provide more accurate medical decisions faster while reducing suffering, waiting times and costs across society, helping to address the pressing unmet health & care needs. AI for digital healthcare contains all the challenges that make AI generally a hard problem, yet to apply AI to healthcare effectively, we cannot use off-the-shelf AI but have to develop methods that are patient-ready and which address the challenges particular to the ethical, legal and regulatory requirements for healthcare. For example, the NHS requires 10,000 data & AI experts over the next five years as part of their recruitment strategy. Thus there is a pressing need for patient-ready AI specialists that can apply their skills, lead efforts and multidisciplinary teams in this heavily regulated domain. To address this need, our CDT and industrial and NHS partners propose training 121 PhD students including clinicians and allied NHS healthcare professions in five cohorts of 24+ students. Our PhD student journey is structured into multiple phases; in the first year-long phase, students will start their research and learn the technical aspects of AI and their specific application to healthcare. We broaden their training bespoke CDT advanced courses and hands-on training that only make sense for vast research cohorts such as ours to introduce them to the complex healthcare sector. These offerings, the academic and technical scope, comprise studies in AI ethics, healthcare practice and regulation, as well as hands-on software carpentry for vast NHS primary care (SAIL) and hospital databases (London SDE), as well as biobanking (UK BioBank) know-how on the technical end such as deep learning, federated machine learning and foundation models. Including these training aspects are key due to the immediate impact that our NHS and industry-aligned projects in the AI in Digital Health Centre for doctoral training can have on the life and health of citizens. Core to our approach is thus not only bespoke cohort-based training but also cohort-lead activities, such as funds for regular international speakers, seminars, hackathons and work-social events, as well as co-location that will keep these cohorts engaged and developing together. Therefore, our cohorts will endeavour to offer a rich mix of students from mathematics, computer science, engineering, physics, physiology, psychology, medical, and non-clinical professionals such as therapists and pharmacists. Our training offering is enhanced through co-created content by our industry partners that can help catalyse spin-offs (Scalespace), regulatory training (MHRA, HRA, BSI), data access (SAIL, LondonSDE, UK Biobank), as well as NHS trusts. Our graduating PhDs research will represent a new generation of diverse AI researchers with backgrounds ranging from computer science to design engineering and clinical medicine that can develop and deploy AI seamlessly across disciplinary boundaries to deliver health and care. We have teamed up with 3 NHS Trusts and clinical institutions to enable direct clinical involvement and impact - promoting on-site research & development of our students' projects and more than doubled the total number of UKRI studentships with studentships contributed by the UK's vibrant Digital Health industry and institutions. Our focus on AI for Digital Healthcare is not only addressing a pressing need but has also been recognised by our 44 partners and institution, which enabled us to multiple the requested UKRI investment three times (3X), evidence of the importance of this CDT.

Organisations

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/Y030974/1 30/09/2024 30/03/2033
2930159 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Elizaveta Sheremetyeva
2930472 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Lucas Kikuchi Iijima
2927659 Studentship EP/Y030974/1 30/09/2024 28/09/2028 Noura Ezaz Nikpay
2928459 Studentship EP/Y030974/1 30/09/2024 28/09/2028 Natasha Jeans
2929842 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Ruxandra Mihai
2930161 Studentship EP/Y030974/1 30/09/2024 28/09/2028 Adam Tlemsani
2929822 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Gloria Lee
2918005 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Vishal Jain
2930157 Studentship EP/Y030974/1 30/09/2024 29/09/2028 William Raftery
2928586 Studentship EP/Y030974/1 30/09/2024 28/09/2028 Pierre Le Floch
2929813 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Michael Trent
2930163 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Chris Vail
2927580 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Jacob Walker
2930152 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Felix Oury
2929840 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Arthur Lefebvre
2929820 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Caroline Rew
2929829 Studentship EP/Y030974/1 30/09/2024 29/09/2028 Anisia Talianu
2930464 Studentship EP/Y030974/1 20/10/2024 20/10/2028 Joshua Fitch
2930479 Studentship EP/Y030974/1 04/11/2024 03/11/2028 Ashvin Gupta
2936059 Studentship EP/Y030974/1 05/11/2024 05/11/2027 Alfred Balston
2945108 Studentship EP/Y030974/1 31/03/2025 30/03/2029 Joshua Placidi