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P02 Artificial Intelligence and Health Inequities in Primary Care: A Scoping Review and Framework
  1. Alexander d’Elia1,
  2. Lucy Frith2,
  3. Mark Gabbay1,
  4. Sarah Rodgers1,
  5. Ciara Kierans1,
  6. Zoe Colombet1
  1. 1Public Health, Policy and Systems, University of Liverpool, Liverpool, UK
  2. 2Centre for Social Ethics and Policy, School of Law, University of Manchester, Manchester, UK


Background Artificial intelligence (AI) can be defined as a computer system with the ability to improve itself without human intervention and is likely to have a significant impact on healthcare over the coming decade. At the same time, health inequity is one of the biggest challenges for the healthcare sector. Primary care is both a driver and a mitigator of health inequities. With AI quickly gaining ground in primary care, there is a need for a holistic understanding of how AI may affect health inequities, both through its effect on the act of providing care and through potential system effects. This is a review of the process of AI implementation in primary care, and the ways it may impact health inequity.

Methods Following a scoping review approach, online databases were searched for literature related to AI, health inequity, and implementation challenges of AI in primary care. Additionally, articles from primary exploratory searches were added, as well as articles identified from references of included sources.

The results were thematically summarised and used to produce a narrative and a conceptual model for the mechanisms in which social determinants of health and AI in primary care could interact to either improve or worsen health inequities.

Two public advisors were involved in shaping the objectives as well as reviewing sources.

Results A total of 1529 sources were identified, of which 85 met the inclusion criteria. The findings were summarised under six different domains, covering both positive and negative effects: 1) access, 2) trust, 3) dehumanisation, 4) agency for self-care, 5) algorithmic bias, and 6) external effects. The five first domains cover mechanisms in the interface between the patient and the primary care system, while the last domain covers care-system-wide and societal effects of AI in primary care. A graphical model has been produced to illustrate this. Community involvement throughout the whole process of designing and implementing of AI in primary care was a common suggestion to mitigate the potential negative effects of AI.

Conclusion AI has the potential to affect health inequities through a multitude of mechanisms, both directly in the patient consultation and through transformative system effects. This review summarises these effects with a holistic, system-wide understanding and provides a base for future research into responsible implementation.

  • Artificial Intelligence
  • Inequalities
  • Primary Care

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