Artificial Intelligence and Racial and Ethnic Inequalities The Artificial Intelligence and Racial and Ethnic Inequalities in Health and Care projects supports research to advance AI and data-driven technologies in health in ways that better meet the needs of minority ethnic populations. The projects are jointly funded by NHSX’s NHS AI Lab and the Health Foundation and enabled by the NIHR.
NHSX’ NHS AI Lab and the Health Foundation have awarded £1.4m to four projects to address racial and ethnic health inequalities using artificial intelligence (AI) to:
- Better understand and enable opportunities to use AI to ensure innovation happens in response to the health needs of minority ethnic groups.
- Contribute to improving the quality, availability and appropriate use of datasets to account for ethnic diversity in the development of AI models.
- Improve the development, testing and deployment of AI models across patient populations to reduce bias and improve the performance and accuracy of emerging and existing tools for different subpopulations.
Following a research call (now closed) NHSX’ NHS AI Lab and the Health Foundation, enabled by the NIHR, have awarded £1.4m to four projects to address racial and ethnic health inequalities using AI.
The NHS AI Lab introduced the AI Ethics Initiative to support research and practical interventions that complement existing efforts to validate, evaluate and regulate AI-driven technologies in health and care, with a focus on countering health inequalities.
Subject to contract, the chosen projects are:
Assessing the acceptability, utilisation and disclosure of health Information to an automated chatbot for advice about sexually transmitted infections in minoritised ethnic populations
Dr Tom Nadarzynski at the University of Westminster
Aims to raise the uptake of screening for STIs/HIV among minority ethnic communities through an automated AI-driven chatbot which provides advice about sexually transmitted infections. The research will also inform the development and implementation of chatbots designed for minority ethnic populations in public health more widely and within the NHS.
I-SIRch - Using artificial intelligence to improve the investigation of factors contributing to adverse maternity incidents involving Black mothers and families
Dr Patrick Waterson and Dr Georgina Cosma at Loughborough University
Aims to use AI to improve the investigation of factors contributing to adverse maternity incidents amongst mothers from different ethnic groups. This research will provide a way of understanding how a range of causal factors combine, interact and lead to maternal harm, and make it easier to design interventions that are targeted and more effective for these groups.
Ethnic differences in performance and perceptions of AI retinal image analysis systems (ARIAS) for the detection of diabetic retinopathy in the NHS Diabetic Screening Programme
Professor Alicja Rudnicka (St. George's Hospital) and Professor Adnan Tufail (Moorfields Eye Hospital and Institute of Ophthalmology, UCL)
Aims to ensure that AI technologies that detect diabetic retinopathy work for all, by validating the performance of AI retinal image analysis systems that will be used in the NHS Diabetic Eye Screening Programme (DESP) in different subgroups of the population. In parallel, the perceptions, acceptability and expectations of health care professionals and people with diabetes will be evaluated in relation to the application of AI systems within the North East London NHS DESP. This study will provide evidence of effectiveness and safety prior to potential commissioning and deployment within the NHS. (Co-investigators: The Homerton University Hospital, Kingston University, and University of Washington, USA)
STANDING together (STANdards for Data INclusivity and Generalisability)
Dr. Xiaoxuan Liu and Professor Alastair Denniston at University Hospitals Birmingham NHS Foundation Trust
University Hospitals Birmingham NHS Foundation Trust and partners will lead STANDING Together, an international consensus process to develop standards for datasets underpinning AI systems, to ensure they are diverse, inclusive and can support development of AI systems which work across all demographic groups. The resulting standards will help inform regulators, commissioners, policy-makers and health data institutions on whether AI systems are underpinned by datasets which represent everyone and don’t risk leaving underrepresented and minority groups behind.