David Clifton
Professor of Clinical Machine Learning
David Clifton is Professor of Clinical Machine Learning in the Department of Engineering Science of the University of Oxford. He is a Research Fellow of the Royal Academy of Engineering, Visiting Chair in AI for Healthcare at the University of Manchester, and a Fellow of Fudan University, China. He runs the Computational Health Informatics Lab within the Department of Engineering Science, which has sites in Oxford (at the Institute of Biomedical Engineering) and China (in the Oxford Suzhou Centre for Advanced Research).
David studied Information Engineering at Oxford's Department of Engineering Science. His previous research resulted in patented systems for jet-engine health monitoring, used with the engines of the Airbus A380, the Boeing 787 "Dreamliner", and the Eurofighter Typhoon. Since 2008, he has focused mostly on healthcare applications, and his current research focuses on the development of machine learning for tracking the health of complex systems. Patents arising from his collaborative research have been commercialised via university spin-out companies OBS Medical, Oxehealth, and Sensyne Health. He holds an EPSRC "Grand Challenge" fellowship for "future leaders in healthcare", and was jointly awarded the inaugural Vice-Chancellor's Prize for Innovation, for interdisciplinary research.
Recent publications
-
Large Language Models in Mental Health Care: A Scoping Review
Journal article
Hua Y. et al, (2025), Current Treatment Options in Psychiatry, 12
-
Beyond Correlations: The Necessity and the Challenges of Causal AI
Preprint
Chauhan VK. et al, (2025)
-
Dynamic Beat-to-Beat Measurements of Blood Pressure Using Multimodal Physiological Signals and a Hybrid CNN-LSTM Model.
Journal article
Xiang T. et al, (2025), IEEE journal of biomedical and health informatics, 29, 5438 - 5451
-
Epidemiology of Thrombotic Thrombocytopenia Syndrome 2011 to 2022: English Sentinel Network Cohort Studies
Journal article
Ordóñez-Mena JM. et al, (2025), Drug Safety
-
An artificial intelligence-based approach to identify volume status in patients with severe dengue using wearable PPG data.
Journal article
Lyle NN. et al, (2025), PLOS digital health, 4