Introduction
Digital and data-driven technologies are now being used in all facets of health, from research, to the doctor’s office, to the operating room and beyond. It is critical to ensure that the entire healthcare system and all populations have access to and benefit equally from these advances, in particular for those who experience barriers to accessing care (and are underrepresented in health data), and those without the skills to access digitally enabled technologies. The Health equity interest group connects data scientists, statisticians, machine learning researchers, public health and health & social care professionals, policy-makers, and entrepreneurs who have a shared interest in using data science to both better understand health inequalities and advance health equity within health and care systems. By bringing together these disciplines, the Interest Group aims to ensure that the latest research and innovations in data science, machine learning, and artificial intelligence improve everyone’s health.
Explaining the science
Advancing health equity is a grand challenge for which there is no well-defined science. While there is not even a consensus definition of ‘health equity’, it is widely agreed that the factors underlying health inequalities are complex and intertwined, involving numerous social, cultural, economic, environmental and genetic components. To tackle this therefore, we are taking a pragmatic and multidisciplinary approach. In a nutshell, we will start with synergies of AI and data science with particular focus areas, including health informatics (led by Dr Honghan Wu); statistical methods (Dr Brieuc Lehmann & Professor Ioanna Manolopoulou); social determinants of health (Prof Alisha Davies); and genomics (Dr Maxine Mackintosh).
Aims
The Health Equity Interest Group aims to form an inclusive multidisciplinary working force to ensure the applications of AI in medicine give everyone equal access to care resources and improve everyone’s health. Specifically, we have the following objectives.
- Connect researchers with public health, health and care professionals to advance health equity by a) developing new methodologies and digital tools to better understand and address existing inequalities, and b) safely applying the latest innovations in data science and AI in healthcare settings.
- Provide a platform to share learnings, best practices and priorities, and equip health policy and practice leaders with the necessary technical skills to assess the potential opportunities and pitfalls of the use of DS and AI tools in health for equity
- Promote discussion between the various stakeholders (academics, public health, health and care professionals, social scientists, regulatory agencies (e.g. MHRA, NICE), health care commissioners, policymakers, funders etc.) to identify the main challenges, risks, and barriers in the equitable use of statistics, machine learning and AI both in biomedical research, in the clinic and at a population level, thus setting the agenda for future research into these areas.
- Engage with public groups to ensure public view on the development and application of DS and AI for health equity are considered by the community, and that the public experience of health equity/inequity also informs the methods developed and highlights potential pitfalls.
How to get involved
Organisers
Dr Brieuc Lehmann
Assistant Professor (University College London) and member of the Turing-RSS Lab
Dr Alisha Davies
Head of Research and Evaluation, Public Health Wales and Co-chair of Clinical AI Interest Group