AI for Health
Uses AI to improve health and well-being
Programme Summary
AI for Health is a research programme dedicated to advancing AI in support of human health and well-being. Our research explores the use of AI across a wide spectrum of health-related applications—from accelerating biomedical discovery and supporting scientific research to enhancing diagnostics, treatment planning, and clinical decision-making. We also investigate how AI can contribute to health promotion and disease prevention through systems that enable early detection, personalised monitoring, and proactive support for healthy lifestyles. This includes the use of multisensory data, wearable technologies, and population-level analysis to better understand and respond to individual and public health needs. By combining expertise in AI, medicine, and the life sciences, the programme aims to generate new insights, methodologies, and tools that advance both the science and practice of health.
Featured
Child and Adolescent Data REsource
Developing personalised, preventative clinical pathways for children’s mental health, with an approach harnessing broad data types, including electronic health record, genetic, deep phenotyping and other data types relating to children and young people.
Initiative Lead: Anna Moore
Cambridge Image Analysis Group
The Cambridge Image Analysis group specialises in theory and methodology development to solve intricate problems, ranging from digital image and video processing to inverse problems and partial differential equations, optimisation algorithms, mathematical modelling, and machine learning, with active interdisciplinary collaborations with clinicians, biologists and physicists on biomedical imaging topics.
Group Lead: Carola Bibiane Schönlieb
Cambridge Human Imaging and Longitudinal Development (CHILD) Study
This study tracks babies of autistic mothers or siblings to understand early autism traits and improve support for autistic mothers and at-risk children from birth using a variety of methods including MRI scanning and infant behavioural developmental assessments.
Study Contributor: John Suckling
