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TRUVACE RECORD VERSION
record: TRV-2026-0645
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-04T06:09:57.231343Z
status: published
lens: trace
sector: health
headline: Between the hype and harm: does artificial intelligence in health offer solace or further exclusion for marginalised populations in Sub-Saharan Africa? A scoping review
dek: Artificial intelligence (AI) is increasingly being integrated into healthcare systems and has the potential to improve health outcomes. In Sub-Saharan Africa (SSA), however, concerns remain that AI may either reduce or exacerbate existing health inequities depending on how it is developed, governed, and implemented. This scoping review aimed to map and synthesise the existing evidence on the implications of AI for health equity among marginalised populations in Sub-Saharan Africa. PubMed, Web of Science, Scopus,…
gain_title: AI applications in healthcare could expand access and improve disease surveillance and health system planning for marginalised populations in Sub-Saharan Africa.
problem_title: AI integration may reinforce health inequities for marginalised populations in Sub-Saharan Africa due to infrastructure gaps, algorithmic bias, under-representation of African datasets, and weak governance.
trace_subject: AI use in healthcare for marginalised populations in Sub-Saharan Africa and its effect on health equity
gain_reading: AI applications in healthcare could expand access and improve disease surveillance and health system planning for marginalised populations in Sub-Saharan Africa.
gain_evidence: AI has the potential to improve health equity by expanding healthcare access, strengthening disease surveillance, supporting health system planning, and improving access to specialised services
problem_reading: AI integration may reinforce health inequities for marginalised populations in Sub-Saharan Africa due to infrastructure gaps, algorithmic bias, under-representation of African datasets, and weak governance.
problem_evidence: AI may reinforce existing inequities through digital infrastructure gaps, algorithmic bias, under-representation of African datasets, weak governance, and data colonialism
quick_read: This scoping review mapped evidence published up to March 2026 on AI in healthcare in Sub-Saharan Africa, focusing on marginalised populations. Searching four databases and grey literature, the authors included 23 sources and synthesised them thematically.

The synthesis matters because it shows the same tools could either narrow or widen health inequities depending on infrastructure, data representation, and governance. By August 2026 the evidence base remained small and largely conceptual, leaving uncertainty about which implementation models actually deliver equitable benefits in practice.
limitation: 
tag: Automated dual reading
key_points: Scoping review searched PubMed, Web of Science, Scopus and grey literature between February and March 2026 using Arksey and O'Malley framework and PRISMA-ScR. | Twenty-three sources met inclusion criteria for AI applications, governance or implementation involving marginalised populations or health systems in SSA. | Authors identified two dominant narratives: potential to improve equity versus risk of reinforcing inequities through infrastructure gaps, bias, and weak governance.
rundown: The review screened peer-reviewed and grey literature on AI in healthcare involving marginalised populations or health systems within SSA, with two reviewers independently screening and extracting data using a standardised charting form.

Findings were synthesised thematically into two narratives, concluding that impact depended on equitable infrastructure, inclusive governance, and context-specific implementation, and calling for investment in digital infrastructure, representative data systems, ethical governance, and inclusive policies.
sources:
- peer_reviewed | Global Health Action | https://doi.org/10.1080/16549716.2026.2710547 | 2026-08-03
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