A new research hub is combining mathematics, statistics, and computer science to build AI tools that analyse patient scans, health records, memory tests, and genetic data together, rather than treating each data type in isolation. Current medical AI often focuses on a single data source—such as an MRI scan—while ignoring the richer picture that emerges when imaging is combined with a patient’s medical history, cognitive test results, or genomic profile. This fragmented approach limits the accuracy of diagnosis and treatment planning. The Cambridge Mathematics of Information in Healthcare (CMIH) hub aims to remove those disciplinary boundaries, creating algorithms that integrate multiple data streams into a single, practical tool for clinicians. If successful, the hub’s methods could improve personalised diagnosis and treatment for the UK’s three leading causes of death and disability: cancer, cardiovascular disease, and dementia. On a population level, the same algorithms could help identify new disease targets and validate treatments. The work is applied rather than purely fundamental—its explicit goal is to bring AI-driven healthcare decision-making directly to clinical end users, potentially changing how doctors interpret complex patient information in routine practice.
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In our work in the current edition of the CMIH we have built up a strong pool of researchers and collaborations across the board from mathematics, statistics, to engineering, medical physics and clinicians. Our work has also confirmed that imaging data is a very important diagnostic biomarker, but also that non-imaging data in the form of health records, memory tests and genomics are precious predictive resources and that when combined in appropriate ways should be the source for AI-based healthcare of the future. Following this philosophy, the new CMIH brings together researchers from mathematics, statistics, computer science and medicine, with clinicians and relevant industrial stakeholder to develop rigorous and clinically practical algorithms for analysing healthcare data in an integrated fashion for personalised diagnosis and treatment, as well as target identification and validation on a population level. We will focus on three medical streams: Cancer, Cardiovascular disease and Dementia, which remain the top 3 causes of death and disability in the UK. Whilst applied mathematics and mathematical statistics are still commonly regarded as separate disciplines there is an increasing understanding that a combined approach, by removing historic disciplinary boundaries, is the only way forward. This is especially the case when addressing methodological challenges in data science using multi-modal data streams, such as the research we will undertake at the Hub. This holistic approach will support the Hub aims to bring AI for healthcare decision making to the clinical end users.
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