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.
Key publications
-
Benchmarking transformer-based models for medical record deidentification: A single centre, multi-specialty evaluation
Preprint
Kuo R. et al, (2025)
-
Continual learning across population cohorts with distribution shift: insights from multi-cohort metabolic syndrome identification.
Journal article
Liu C. et al, (2025), Journal of the American Medical Informatics Association : JAMIA
-
Using machine learning to predict anticoagulation control in atrial fibrillation: A UK Clinical Practice Research Datalink study
Journal article
Gordon J. et al, (2021), Informatics in Medicine Unlocked, 25
Recent publications
-
Continual learning across population cohorts with distribution shift: insights from multi-cohort metabolic syndrome identification.
Journal article
Liu C. et al, (2025), Journal of the American Medical Informatics Association : JAMIA
-
Dysregulated immune proteins in plasma in the UK Biobank predict Multiple Myeloma 12 years before clinical diagnosis
Journal article
Fieggen J. et al, (2025), Blood Advances
-
Benchmarking transformer-based models for medical record deidentification: A single centre, multi-specialty evaluation
Preprint
Kuo R. et al, (2025)
-
Application of large language models in medicine
Journal article
Liu F. et al, (2025), Nature Reviews Bioengineering
-
A scoping review of large language models for generative tasks in mental health care.
Journal article
Hua Y. et al, (2025), NPJ digital medicine, 8