Digital and AI technologies
Our large multidisciplinary community explores the potential for digital and AI technologies to revolutionise health and care.
We have more than 350 academics from across the University involved in our multidisciplinary Digital Health and Care network, which is part of the University’s broad Digital Futures initiative.
This broad base of researchers and ‘intrapreneurs’ allows us to conduct pioneering research that explores the use of digital and AI technologies to:
- understand the causes and progressions of diseases;
- develop new diagnostics and therapies;
- understand the complex interaction between health and environment;
- optimise the health and care system;
- develop new experimental methods;
- validate digital health technology.
The research takes three broad perspectives:
Algorithms
We develop innovative methods for analysing health and care data in order to discover new mechanisms, predict and detect disease onset, and model individual responses to treatment.
In this area, the University’s partnership with the Turing Institute is particularly important. The University has strengths in the use of real-world health data for predictive, precision and personalised medicine, in text analytics, and in imaging.
Applications range from cancer to musculoskeletal disease, and cardiovascular disorders.
Systems
We explore deployment of digital interventions in order to transform health and care services. This area typically draws upon algorithmic outputs from the first area to create person-centred care pathways.
Notable strengths exist in electronic audit and feedback, clinical decision support, and learning health systems, with most applications in primary care.
People
We research the use of digital technologies to improve the health and well-being of individuals through personal digital devices.
This area mostly focuses on prevention and management of long-term conditions. We have significant strengths in mobile health, wearable sensing, and behaviour change.
This area typically draws upon new algorithms, whilst in turn providing new sources of data and challenges for that area.
These perspectives are underpinned by research in four technology areas:
Artificial intelligence
Artificial intelligence has the capability to improve health outcomes by supporting clinical decision-making. Recent breakthroughs in machine learning have revived excitement for this type of technology, and sparked new promises of radical improvement of healthcare services.
Risk prediction
Healthcare services worldwide are moving away from being reactive and focused on illness, and have embraced approaches that are proactive, preserve health, and prevent illness. This requires the ability to predict illness and disease progression before they actually happen – to predict risk.
Connected health
Connected health is the broad term for using network-based technologies to provide healthcare services remotely. It encompasses telehealth, mobile health, and the use of environmental, home-based and wearable sensor technologies for health. It has the potential to radically change healthcare delivery.
Learning health systems
Health systems, at any level of scale, become learning systems when they develop the ability to, continuously and routinely, study and improve themselves. Digital technologies have the potential to trigger this ability by defining, structuring, creating and reusing data and knowledge.
Digital Theme Lead: Professor Niels Peek
Professor Peek is the Director of the Christabel Pankhurst Institute and University lead for Digital Health and Care. He has a background in computer science and artificial intelligence. His research focuses on data-driven informatics methods for healthcare quality improvement, data mining for healthcare, predictive models, and computerised decision support.
AI Theme Lead: Professor Sami Kaski
Professor Kaski is Professor of Artificial Intelligence at The University of Manchester and Aalto University, Director of the Finnish Centre for Artificial Intelligence FCAI. He is an international expert in foundations of AI, with strong interests in applications in health and care.
