Digital Health
The University of Glasgow’s School of Computing Science advances Digital Health by combining Statistical Machine Learning, Human-Computer Interaction and Privacy-Preserving AI to develop effective, trustworthy clinical technologies.
In Computational Biology and Clinical Systems, research spans Precision Medicine, Systems Biology and matching algorithms for organ exchange. Sequence analysis and probabilistic modelling support understanding of cancer and viral evolution and the development of targeted treatments.
Medical Imaging research combines image analysis, generative models, optical brain imaging and multimodal data fusion to improve detection and monitoring of cardiovascular disease, brain cancer and neurological conditions.
Behavioural AI and Socially Assistive Robotics use multimodal learning and affective computing to understand human behaviour and neurophysiology, supporting dementia care, autism and depression monitoring. Work on Brain-Computer Interfaces, human motion analysis and personalised assistive devices aims to improve independence and quality of life, complemented by medical literature analysis and real-time health monitoring.
Research in Information Retrieval, Representation Learning, Algorithmic Fairness and Privacy-Preserving AI addresses security, bias and transparency challenges in healthcare data.
Across these areas, interdisciplinary collaboration with neuroscience, oncology and social sciences helps translate computational advances into practical, patient-centred healthcare technologies.
Project Highlights
KidneyAlgo: New Algorithms for UK and International Kidney Exchange 2023-2025. EPSRC
Prof David Manlove
This collaborative project with Durham University involves developing new algorithms and software for the UK Living Kidney Sharing Scheme (UKLKSS) and for other kidney exchange programmes (KEPs) around Europe. The new algorithms will allow for more complex exchange structures, larger pools and different optimality objectives within the UKLKSS. For international kidney exchange programmes that involve collaboration between multiple countries, the algorithms will ensure fairness and stability, incentivising countries to participate in a shared KEP.
Understanding and predicting brain tumour treatment response using AI 2026-2029, BRUK
Dr Ali Gooya
Glioblastoma is an aggressive, incurable type of brain tumour that strikes around 2,500 people every year in the UK. Only a quarter of patients survive more than a year from diagnosis. In this project, we will be exploring whether a new combination treatment is more effective than the current standard for patients with glioblastoma. We will also explore whether the of use AI and MRI brain scans can create personalised tumour progress maps to forecast how each patient’s tumour may evolve
PHAWM: Participatory Harm Auditing Workbenches and Methodologies, 2024-2028, RAI-UK
Prof Simone Stumpf
A significant barrier to reaping the benefits of predictive and generative AI is their unassessed potential for harms. Hence, AI auditing has become imperative, in line with existing and impending regulatory frameworks. Yet, AI auditing has been haphazard, unsystematic, and left solely in the hands of experts. Our project, bringing together a consortium of 7 academic institutions and over 20 partner organisations, aims to fix this fundamental challenge through the novel concept of participatory AI auditing where a diverse set of stakeholders without a background in AI, such as domain experts, regulators, decision subjects and end-users, undertake audits of predictive and generative AI, either individually or collectively