Multi-million EPSRC funding for KCL DRIVE-Health CDT
March 13, 2024
DRIVE-Health has been awarded £7.9 million from The Engineering & Physical Sciences Research Council (EPSRC) for student intake from 2024 onwards. DRIVE-Health is one of 65 CDTs which received funding, totalling more than £1 billion.
Using seed funding from King’s Centre for Doctoral Studies awarded in 2020, DRIVE-Health has trained 30 students to date. Building on this, the new award will support five additional cohorts at King’s, totalling at least 85 talented PhD students. The CDT is expecting to welcome its fourth intake of at least 15 students in October 2024.
DRIVE-Health is the first health data science training centre in the UK to harness cross-sector collaboration across the NHS, industry, enterprise, policy makers, and academia. Working with diverse partners, DRIVE-Health PhD students develop cutting-edge models which leverage healthcare data to improve patient outcomes, streamline operations, and enhance clinical decision-making processes.
EPSRC CDT DRIVE-Health’s vision is informed by three core goals:
- To provide world-class training in health data science research to the next generation of health data scientists, who will have the multidisciplinary skills needed to enable transformations in public health and breakthrough treatments.
- To solve the most challenging problems in data-driven health research through a diverse community of the brightest minds in health data science and an open, collaborative culture which fosters exchange and champions innovation.
- To co-create a translational cross-sector collaboration with the NHS, industry, enterprise, policy makers and academia.
Professor Richard Dobson, Co-Director of DRIVE-Health and Professor of Medical Informatics at King’s IoPPN, says
"As more data from biological, social, genomic, imaging, smart devices, and electronic health records becomes available, there are significant opportunities to revolutionise the way healthcare is delivered. Through DRIVE-Health, we will train some of the brightest minds in health data science to develop cutting-edge tools which utilise data to improve healthcare systems and patient outcomes."
"This is an exciting time for medicine, with new data paradigms creating a novel research and implementation landscape covering the full span from cell to society. Over the next nine years, DRIVE-Health will nurture world-class researchers that will chart that landscape and drive the UK’s health data agenda." Professor Vasa Curcin, Co-Director of DRIVE-Health and Professor of Health Informatics at King’s FoLSM.
The DRIVE-Health PhD Programme (2024-2032) focuses on five key scientific research themes:
- Sustainable health data systems engineering: Investigates methods to develop secure and scalable software systems for healthcare. Theme lead: Dr Zina Ibrahim.
- Multimodal patient data streams: Integrates diverse patient data types for analysis, including wearables and electronic health records. Theme lead: Dr Jorge Cardoso.
- Complex simulations and digital twins: Builds simulated environments to train AI models for healthcare applications. Theme lead: Dr Steffen Zschaler.
- Next-generation clinical user interfaces: Ensures healthcare data science applications are usable in clinical settings. Theme lead: Professor Nick Holliman.
- Co-designing impactful patient-centric healthcare solutions: Co-producing and co-designing healthcare solutions to maximise impact across all themes. Theme lead: Professor Claire Steves.
On top of the £7.9m provided by the EPSRC, DRIVE-Health has received over £5.1m from partners, as well as in-kind contributions worth nearly £4m.
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We are looking forward to welcoming Professor Honghan Wu, Professor of Health Informatics and AI at the University of Glasgow, who will deliver his talk “Large language model and Radiology: how to facilitate human and AI collaboration? " as part of our Seminar Series. Abstract: In this upcoming talk, Professor Honghan Wu explores the essential shift from viewing AI as a potential replacement for radiologists to recognizing it as a critical collaborative partner. Moving beyond basic tasks like detection and triage, the presentation highlights how AI can address practical clinical "pain points," such as reducing automated protocoling time by up to 60% and decreasing the time spent communicating with providers and patients by 30%. Professor Wu will present recent research on using knowledge-retrieval and Large Language Models for clinical report error correction and generation. The session concludes with an examination of the real-world deployment lifecycle, discussing the challenges of monitoring the over 700 FDA-cleared radiology AI devices currently in practice Seminar Series Event : “Large language model and Radiology: how to facilitate human and AI collaboration?" Date and Time: Thursday 25 June 2026, 15:00 – 16.00 hrs (BST) Location: Large Committee Room, Hodgkin Building, Guy's Campus Attendance: Mandatory for all DRIVE-Health students; a calendar invitation has already been sent. Registration: Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend. Biography Honghan Wu is a Professor of Health Informatics and AI, based in the School of Health and Wellbeing of the University of Glasgow, where he leads the research theme of data science and AI. Prof Wu is a co-director of Health Data Research Scotland. He also is an honorary professor at Hong Kong University, an honorary associate professor at Institute of Health Informatics, UCL, and a former Turing Fellow of The Alan Turing Institute, UK's national institute for data science and artificial intelligence. Prof Wu holds a PhD in Computing Science. His current research focuses on machine learning, natural language processing, knowledge graph and their applications in medicine.
We are pleased to welcome Simon Ellershaw, PhD Candidate at University College London (UCL) as part of the UKRI UCL Centre for Doctoral Training in AI-enabled Healthcare Systems, who will deliver his talk “Developing Healthcare LLMs: From the NHS to Silicon Valley " as part of our Seminar Series. Abstract: This talk links my PhD and my Silicon Valley internship through one theme: what it really takes to build and deploy LLMs in healthcare. I will introduce Foresight England (Foresight E), a national-scale generative foundation model trained from scratch on 54.9 million de-identified longitudinal NHS EHRs to model patient timelines and enable zero-shot prediction across around 40,000 coded medical events. As NHS England has paused data access pending review, I will focus on the core methodology and lessons learned. I will then switch to my Parexel internship in San Francisco, where I worked in the company’s AI lab on production-focused applications, including pharmacovigilance and protocol de-risking. I will explain how I ended up there, what I worked on, and what I learned, with a candid view of what day-to-day life and work in the Bay Area actually looks like. I will also reflect on how the recent generative AI boom has reshaped the problems teams like ours choose to tackle and the way this work gets built, evaluated, and shipped. Seminar Series Event : “Developing Healthcare LLMs: From the NHS to Silicon Valley" Date and Time: Wednesday 27 May 2026, 15:00 – 16.00 hrs (BST) Location: Judy Dunn, SGDP Building, Denmark Hill Campus Attendance: Mandatory for all DRIVE-Health students; a calendar invitation has already been sent. Registration: Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend. Biography Simon Ellershaw is a PhD Candidate at University College London (UCL) as part of the UKRI UCL Centre for Doctoral Training in AI-enabled Healthcare Systems, supervised by Prof Richard Dobson and Dr Anoop Shah. His research spans LLM-based generation of hospital discharge summaries, national-scale pre-training of generative models on 57 million electronic health records, and post-training using real-world patient outcomes as verifiable reinforcement-learning rewards. Alongside his PhD, he interned at Parexel AI Labs and now works part-time as an NLP Engineer, developing and deploying production LLM/NLP systems, including applications in pharmacovigilance and quality assurance.
