Our AI-driven analysis of NHS data has the potential to accelerate discovery in life sciences exponentially, leading to new treatments and better care for patients.
By combining a deep understanding of clinical practice with established and novel AI methods, we make sense of huge quantities of anonymised patient data and provide actionable insights for the life sciences industry.
We have assembled a world-class team from the disciplines of data science, medical science and clinical practice, with unparalleled access to leading experts in these fields.
Our team includes some of the best talents from top educational and research institutions around the world, with published papers in eminent scientific journals and a passion for pushing the boundaries of scientific and medical discovery.
Through years of collaboration between the University of Oxford and Oxford University Hospitals NHS Trust, we have developed an unrivalled knowledge of curating complex anonymised patient data and uncovering meaningful patterns in time-series data, including panomics data.
Our Scientific Advisory Board and wider network includes world experts in a range of disease areas including IBD, oncology, cardiovascular, respiratory, diabetes and immunology.
These experts work closely with our Clinical AI team to challenge and validate from a clinical perspective, guiding the team to target unmet therapeutic needs and make breakthrough discoveries that have a direct impact on patient care. All at an unprecedented pace.
(Simon Travis / Tim Hinks explains how AI can accelerate drug development and what this means for the NHS and for healthcare.)
Our clinical AI expertise is underpinned by published articles in tier-one journals across multiple fields of AI and medical research. View all publications.
Gaussian Processes for Personalized Interpretable Volatilty Metrics in the Step-Down Ward
DeepAMR for Predicting Co-occurrent Resistance of Mycobacterium Tuberculosis
Comparing Different Ways of Calculating Sample Size for Two Independent Means: A Worked Example