Removing bias from healthcare AI tools

Rapid advances in Artificial Intelligence (AI) have opened the way for the creation of a huge range of new healthcare tools, but to ensure that these tools do not exacerbate pre-existing health inequities, researchers urge the use of more representative data in their development. Researchers from Oxford University's Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDORMS) , University College London and the Centre for Ethnic Health Research , supported by Health Data Research UK , have for the first time studied the full detail of ethnicity data in the NHS. They outline the importance of using representative data in healthcare provision and have compiled this information into a research-ready database. The new study, published in Nature Scientific Data , is the first part of a three-phase project that aims to reduce bias in AI health prediction models which are trained on real-world patient data. The project, which addresses ethnicity disparities that were highlighted during the pandemic, is part of the UK Government's COVID-19 Data and Connectivity National Core Study led by Health Data Research UK. The researchers used de-identified data on ethnicity and other characteristics from general practice and hospital health records, accessed safely within NHS England's Secure Data Environment (SDE) service, via the British Heart Foundation Data Science Centre 's CVD-COVID-UK/COVID-IMPACT Consortium. This is the first time that patient ethnicity data has been studied at this depth and breadth for the whole population of England.