Medical Machine Learning
Medical machine learning involves the development of new algorithms and models which can interpret medical data and improve clinical diagnosis and prognosis.
Our researchers work closely with clinical teams across a broad range of health areas, including:
- cancer
- cardiology
- genomics
- epidemiology and public health
- neurology
- obstetrics and gynaecology
- ophthalmology
- orthopaedic surgery
- paediatric disease
- psychology and Psychiatry
- respiratory disease
- rheumatology
We're always happy to talk about new collaborations.
Our research
Machine learning can be applied to many clinical tasks and problems. Our use of machine learning approaches encompasses the discovery of new biomarkers and predictive models that predict disease diagnosis, prognosis, and response to therapy. We are focussed on novel applications of machine learning methods to clinical problems, and on the translation of our work to into clinical environments. We are also working on responsible medical AI – the safe, ethical, and equitable application of machine learning in clinical settings.
Our team
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Academics
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Postdoctoral researchers
Dr Nikhil Cherian
Dr Samra Naz
Dr Sabrina Sghirripa
Dr Minh-Son To -
Students
Dr Tristan Bampton
Dr Alix Bird
Georgia Kenyon
Luke Smith
Lana Tikhomirov
Jordan Vihermaki
Luke Whitbread
Minyan Zeng