Dr Samuel Relton
- Position: Professor in Health Data Science
- Areas of expertise: artificial intelligence; data science; clinical prediction models; health service evaluation; electronic healthcare records; big data; high-performance computing; machine learning; mathematics
- Email: S.D.Relton@leeds.ac.uk
- Phone: +44(0)113 343 6731
- Location: 10.31 Worsley Building
- Website: Personal site | X | ORCID
Profile
Sam Relton is a Professor in Health Data Science at the Leeds Institute of Health Sciences, University of Leeds. With a strong background in statistics, computer science, and artificial intelligence, his research focuses on leveraging electronic healthcare records to make meaningful, real-world improvements to patient care across the NHS. Rather than developing flashy models for the sake of it, Sam is passionate about creating practical, impactful solutions that optimise healthcare service provision and improve treatment outcomes. His work spans various clinical areas, with a particular emphasis on multimorbidity, healthy ageing, and the intersection of physical and mental health. He also has a keen interest in the regulation and rigorous evaluation of AI in health, ensuring that clinical prediction models remain safe, transparent, and genuinely beneficial to patients and clinicians alike.
Responsibilities
- Lead for Health Data Science, Leeds Institute for Health Sciences
- Community Co-lead, LIDA Health
Research interests
Sam’s research interests centre around using electronic healthcare records, statistics, and artificial intelligence to solve genuine clinical problems. He focuses on complex areas such as healthy ageing, multimorbidity, and the overlap between physical and mental health, with the goal of creating tools that actually work on the ground. He is deeply committed to ensuring that data science translates into meaningful, tangible improvements for patient care and health service optimisation. More recently, he has focused on the safety, ethics, and regulation of AI in medicine.
Qualifications
- PhD in Numerical Analysis and Scientific Computing, University of Manchester
- BSc in Mathematics, University of Manchester
Professional memberships
- SIAM
- IMA
Student education
I plan and run various teaching sessions as part of modules for students both in MBCHB and MSc Health Informatics courses. These primarily focus on statistics, machine learning, and other topics related to data acquisition, handling, and analysis.
Research groups and institutes
- Leeds Institute of Health Sciences
- Health Services Research Division
- Health services research