AI and Medical Big Data

We will harness the twin advantages of our large clinical datasets and strong medical expertise to establish a leading position in AI research across a wide range of medical areas (such as cardiovascular, metabolic, molecular and cellular medicine). This will advance the prevention, precision diagnosis and targeted treatment of disease. 

What challenges will be addressed?

1. How to better understand shared disease mechanisms through the lens of big data and deep learning AI. This includes diseases such as cardiac disease, metabolic disease, stroke, cancer and multi-morbidity; mechanisms such as causation, phenotyping, associations, and risk predictions; big data such as from retrospective, archived databanks and perspective biomedical studies.

2. How to use AI to build precision diagnostic and prognostic tools. These tools will leverage analysis of multidisciplinary big databanks available within RDM from clinical practice and research, such as imaging, cellular, digital pathology, molecular, -omics, genetics, health records and other biomarkers.

3. How to harness novel AI models for precision therapeutics. Using the enhanced understanding of disease mechanisms (1) and with guidance from novel precision diagnostics/prognostic algorithms (2), the theme will be able to support the vision for the development of targeted therapies for those who need them.

how will collaboration enable success?

Using very big data, AI can unveil interactions and associations between disease, imaging, cellular and molecular activities, beyond conventional medical and statistical approaches. This means it can foster new collaborations between large cohorts of researchers within RDM working on diagnostic imaging, metabolism, cellular and molecular research.

Within this cross-cutting research theme, RDM will continue to collaborate with Oxford Population Health (NDPH). NDPH and its Big Data Institute focus on the quantitative analysis of large scale multimodal biomedical databanks; RDM excels in discovery science and precision diagnostics and medicine. We will enhance the partnership and the translation of novel discovery and diagnostic tools at big data and population scale.

Collaborative activities across multiple University departments will facilitate the exchange of biomedical and AI technical expertise, and the validation and translation of novel machine learning methodology in clinical data and practice.

We will also enable or strengthen industrial partnerships, many of these with Oxford spinouts.