AIM – Over the next three years, we will develop a versatile AI foundation model that integrates health records, medical images, and genomic data from the UK Biobank. Our goal is to set a new standard in predicting disease risk, forecasting outcomes, and uncovering biological drivers of both common conditions (like cancer) and rare disorders.
BACKGROUND & RATIONALE – Today, clinicians must sift through vast amounts of patient history, imaging scans, lab tests, and genetic information to make treatment decisions. As biomedical data grows, this process becomes more time-consuming and prone to oversight. AI offers the promise of identifying hidden patterns across diverse data types, but existing tools rarely handle records, images, and genomic sequences all at once-especially at the scale of hundreds of thousands of patients. By training our model on such a large, varied population, we expect it to perform accurately for individuals from many genetic and environmental backgrounds.
PUBLIC HEALTH IMPACT – The conditions represented in the UK Biobank affect millions worldwide. Our model will help predict who is at highest risk, estimate likely disease trajectories, and suggest underlying molecular causes. We will share our code and results openly, so other researchers can build on our work. Ultimately, this will lead to earlier testing and diagnosis, more personalized treatment plans, and new insights that drive the discovery of novel therapies.
