Predictive Medicine 

Predictive medicine is the development of tools to quantify and predict the probability of a disease, its treatment, and its impact on the individual. 

Sheffield BRC embeds digital-data and technologies integrated with computational, physical and AI models into clinical trials of new treatments. These approaches are core to Sheffield’s Insigneo Institute for in silico Medicine, an internationally recognised centre-of-excellence with over 280 members (see below). Computational models and diagnostic /predictive AI algorithms from routine X-ray, angiography and CT images are at the heart of our approaches. We target a reduction in clinical trial size and de-risk drug development in diseases across our BRC Themes through cost-effective methodologies and sensitive endpoints (imaging, functional biomarkers, predictive modelling).

Computer Generated Images from a gait analysis study showing a human walking

in silico medicine, also known as ‘computational medicine’, indicates modelling and simulation technologies that directly contribute to the prevention, diagnosis, prognosis, treatment planning and execution, and management of disease. in silico medicine technologies provide subject-specific predictions of quantities that are difficult or impossible to measure directly, but that are important to support the medical decisions about that patient.


Visit Professor Tim Chico's webpage on The University of Sheffield website

Professor Timothy Chico

Predictive Medicine Sub Theme Lead

Dr. Andrew Swift 

Dr. Paul Armitage 

Prof. Damien Lacroix 

Prof. Steven Sourbron 

Prof. Visakan Kadirkamanathan

Prof. Julian Gunn 

Prof. Karl Herholz 

Dr. Haiping Lu  

Prof. Stephen Matcher 

Prof. Allan Lawrie 

Prof. Ivan Minev

Prof. David Kiely 

Prof. Daniel Coca 

Prof. Li Su 

Dr. Paul Morris

Dr. Samer Alabed

Dr. Mahnaz Arvaneh

Dr. Alex Rothman