UKRI Centre for Doctoral Training in Biomedical Artificial Intelligence

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Informatics


Addressing the health needs of a growing and ageing population is a central challenge facing modern society. Technology is enabling the collection of increasingly large and heterogeneous biomedical data sets, yet interpreting such data to gain knowledge about disease mechanisms and clinical and preventative strategies is still a major open problem. Artificial Intelligence (AI) techniques hold huge promise to provide an integrative framework for extracting knowledge from data, with a high potential for fundamental and clinical breakthroughs with significant impact both on public health and on the future of the UK bioeconomy.

The ambition of the proposed CDT is to train a cadre of highly skilled interdisciplinary scientists who will spearhead the development and deployment of AI techniques in the biomedical sector. Achieving our long-term aims will require several hurdles to be overcome. The biomedical sector poses unique methodological challenges to AI technology, due to the need of interpretable models which can quantify uncertainties within predictions. It also presents formidable cultural and technical language barriers, requiring honed communication skills to overcome disciplinary boundaries. Perhaps most importantly, it requires researchers and practitioners with a keen awareness of the societal, legal and ethical dimension of their research, who are able to reach out to societal stakeholders, and to anticipate and engage with the potential issues arising from deploying AI technology in the biomedical sector.

We will realise our ambition through a structured training programme: students will initially acquire the foundational skills in a Master by Research first year, which includes taught courses on the technical, biomedical and socio-ethical aspects of biomedical AI, and provides multiple opportunities to directly experience interdisciplinary research through rotation projects. Students will then acquire in depth research experience through an interdisciplinary PhD, bridging between the University of Edinburgh's world-leading institutions pursuing informatics and biomedical research. Students will benefit from a large and exceptionally distinguished faculty of potential supervisors: over 60 academics including several fellows of the Royal Society/ Royal Society of Edinburgh, and over forty recipients of prestigious fellowships from the ERC, the research councils, and biomedical charities such as the Wellcome Trust. This training programme will be interleaved with intensive training in interdisciplinary communication and science communication, and will offer multiple opportunities to engage with external stakeholders including industrial and NHS internships.

Planned Impact

Our primary goal is to train a cadre of responsible innovators who will spearhead the technological development, implementation and public perception of Artificial Intelligence in the biomedical sector. The impact of this proposal is likely to be significant and widely felt across stakeholders. It will benefit the general public and the NHS, through the development of more effective and cheaper healthcare in the long term. Industrial stakeholders will benefit, through the availability of a cohort of specialists with highly relevant skills, with industry being an active participant and co-creator of the training programme. Academia will benefit, through the production of world leading research, disseminated through REF-eligible publications, and through stronger and lasting links with external stakeholders to be established through our engagement programme.

Our core impact can be further specified along the major lines of training we envisage in the programme. We expect novel AI methodologies integrating heterogeneous data to be instrumental in generating novel models of disease mechanism that will result, in the medium-to-long term, in the development of novel diagnostic tools and novel therapeutic strategies. Our impact will be felt over the industrial sector through the development and delivery of such innovations, and across the general population through the availability of better and cheaper health care. Major diseases where we can expect biomedical AI to have significant impact include, but are not limited to, cancer, cardiovascular, neurodegenerative and infectious diseases, where our strong links with clinical scientists maximise the potential for rapid outcomes. Our industrial links and extensive programme of engagement also maximise the potential for impact on the industrial sector thereby underpinning the UK Bio-economy, and in particular the med-tech and pharma sectors (both worth several billions of pounds annually to the UK economy). Such impacts will be achieved across different time-scales, with some more immediate achievements potentially already emerging during the life-time of the CDT, and most lasting impacts becoming apparent over the subsequent careers of our students as they enact their training in the wider society, applying and disseminating their research over the next two-to-three decades.

We expect our graduates also to achieve major impact by their ability to shape and lead on the socio-ethical aspects of biomedical AI. This will be felt across all levels of activities: throughout their day-to-day research, by promoting an ethics of responsible innovation both for themselves and their peers outside of the CDT; throughout their engagement with clinical scientists and policy makers, where our students will use their cross-disciplinary communication training to fully inform and engage with the issues arising from their research; and, throughout their engagement with the wider community of end-users, where our students will act as ambassadors for AI research and will be able to engage and respond to public concerns. This impact will be achieved both during the life-time of the CDT, through our substantial programme of training through engagement, and in the decades to follow through the lifework of our graduates.


10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S02431X/1 31/03/2019 29/09/2027
2260828 Studentship EP/S02431X/1 31/08/2019 30/08/2023 Domas Linkevicius
2259334 Studentship EP/S02431X/1 31/08/2019 28/02/2024 Katarzyna Szymaniak
2259327 Studentship EP/S02431X/1 31/08/2019 15/02/2024 Matus Falis
2260782 Studentship EP/S02431X/1 31/08/2019 31/01/2024 Alessandro Fontanella
2259350 Studentship EP/S02431X/1 31/08/2019 29/09/2025 Emanuela Molinari
2259346 Studentship EP/S02431X/1 31/08/2019 30/08/2023 Natalia Szlachetka
2260794 Studentship EP/S02431X/1 31/08/2019 30/08/2023 Nikitas Angeletos Chrysaitis
2260824 Studentship EP/S02431X/1 31/08/2019 30/08/2023 Evgenii Lobzaev
2259281 Studentship EP/S02431X/1 31/08/2019 30/08/2023 Rayna Andreeva
2260830 Studentship EP/S02431X/1 31/08/2019 30/11/2023 Peter Hebden
2259340 Studentship EP/S02431X/1 31/08/2019 31/10/2023 Michael Stam
2422869 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Rohan Gorantla
2422814 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Ella Eve Davyson
2422871 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Marcin Daniel Kedziera
2422796 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Filippo Carlo Corponi
2422956 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Craig Nicolson
2422960 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Matthew Thomas Whelan
2422852 Studentship EP/S02431X/1 31/08/2020 29/04/2025 Salvatore Esposito
2422750 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Leonardo Vincenzo Castorina
2422927 Studentship EP/S02431X/1 31/08/2020 30/08/2025 Alexander William Nelson
2422912 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Olivier Labayle Pabet
2422924 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Bryan Man Li
2422817 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Justin Engelmann
2422809 Studentship EP/S02431X/1 31/08/2020 30/08/2024 Jan Krzysztof Dabrowski
2584666 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Dominic Phillips
2584679 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Barry Ryan
2584683 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Fiona Smith
2584648 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Charlotte Merzbacher
2584641 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Thibaut Goldsborough
2584699 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Yongshuo Zong
2584689 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Xiao Yang
2584632 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Raman Dutt
2584685 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Aleksandra Sobieska
2584637 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Hans-Christof Gasser
2584644 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Sebestyén Kamp
2584677 Studentship EP/S02431X/1 31/08/2021 30/08/2025 Benjamin Philps
2731383 Studentship EP/S02431X/1 31/08/2022 30/08/2030 Stefani Tirkova
2731381 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Maria Dolak
2731387 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Szu-Szu Ho
2731388 Studentship EP/S02431X/1 31/08/2022 30/08/2029 Scott Pirrie
2731385 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Lars Werne
2731386 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Michal Kobiela
2731357 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Dominik Grabarczyk
2731361 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Ke Wang
2731359 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Passara Chanchotisatien
2731356 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Wolf De Wulf
2731360 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Aryo Gema
2731355 Studentship EP/S02431X/1 31/08/2022 30/08/2026 Achille Fraisse