Bridging the digital divide: artificial intelligence as a catalyst for health equity in primary care settings

Abstract

Background
Health inequalities remain one of the most pressing challenges in contemporary healthcare, with primary care serving as both a gateway to services and a potential source of disparities. Artificial intelligence (AI) technologies offer unprecedented opportunities to address these inequities through enhanced diagnostic capabilities, improved access to care, and personalised interventions.

Objective
This comprehensive narrative review aimed to synthesise current evidence on AI applications in primary care settings, specifically targeting health inequality reduction and identifying both opportunities and barriers for equitable implementation.

Method
Following PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we employed a systematic approach to literature identification, selection, and synthesis across seven electronic databases covering literature from 2020 to 2024. Of 1,247 initially identified studies, 89 met inclusion criteria with 52 providing sufficient data quality for evidence synthesis.

Results
The review identified promising applications such as AI-powered risk stratification algorithms that improved hypertension control in low-income populations, telemedicine platforms reducing geographic barriers in rural communities, and natural language processing tools facilitating care for non-native speakers. However, significant challenges persist, including algorithmic bias that may perpetuate existing inequities, the digital divide excluding vulnerable populations, and insufficient representation in training datasets. Current evidence suggests that whilst AI holds transformative potential for advancing health equity, successful implementation requires intentional co-design with affected communities, robust bias mitigation strategies, and comprehensive digital literacy programmes.

Conclusion
Future research must prioritise equity-centred AI development, longitudinal outcome studies in diverse populations, and policy frameworks ensuring responsible deployment. However, careful consideration of unintended consequences, including potential overdiagnosis, erosion of human clinical judgement, and inadvertent exclusion of vulnerable populations, is essential to prevent AI from exacerbating existing health disparities. The paradigm shift towards equity-first AI design represents a critical opportunity to leverage technology for social justice in healthcare.