Lei Clifton
Official Fellow in AI & ML, Reuben College
Statistics and AI for clinical studies
My research interest is at the interface of medical statistics and AI, with over 20 years of research experience.
I am the Programme Director of the MSc in Applied Digital Health, where I am also module leader and lecturer, working alongside the Academic Directors, Profs. John Powell and Catherine Pope.
My research focuses on a wide range of methodology, including foundation models (and large language models) for healthcare, disease prediction, and the fusion of AI with medical statistics. Much of this work collaborates closely with the "AI for Healthcare" group in the Department of Engineering Science, where I also hold an affiliation.
After studying engineering and machine learning, I did my postdoctoral training at the Department of Engineering Science (2009-14), before training as a medical statistician under Prof. Doug Altman at the Centre for Statistics in Medicine (2014-18). Subsequently, I was team leader in the Nuffield Department of Population Health (2019-24), where I led a programme of research in translational epidemiology.
When at home, I can be found painting watercolours, practising yoga, and making noise on the violin with friends.
Recent publications
When to and when not to use machine learning in risk prediction models.
Journal article
Clifton L. et al, (2026), Lancet Digit Health
Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals
Journal article
Gu X. et al, (2026), Nature Machine Intelligence, 8, 220 - 233
ssociations between disease-specific polygenic risk scores and disease-specific causes of death in the UK Biobank cohort
Preprint
Liu W. et al, (2025)
Multimorbidity, disease clusters and risk of all-cause and cause-specific mortality: a population-based prospective cohort study
Journal article
Littlejohns TJ. et al, (2025), Scientific Reports, 15
Transforming label-efficient decoding of healthcare wearables with self-supervised learning and “embedded” medical domain expertise
Journal article
Gu X. et al, (2025), Communications Engineering, 4
Collaborators
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John Powell
Professor of Digital Health
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Catherine Pope
Professor of Medical Sociology
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Andrew Farmer
Professor of General Practice