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Review of 23 sources examines AI in health care for marginalised groups in Sub-Saharan Africa

Study title: 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

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.

Read the study abstract

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,…

By the Truvace desk1 min read1 sourceEvidence: Emerging · 22/100 — Commentary, opinion, or narrative review · n = 23 · 1 independent sourcePeer-reviewedTRV-2026-0645· cite & verify
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
A bill to amend chapter 17 of title 38, United States Code, to establish a program for rural health-care clinics, and for other purposes by United States. Congress. Senate United States. Congress. Senate. Committee on Veterans' Affairs. Public domain
Trace impact readingContested
P 45The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 45The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

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.

Main points

  1. 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.
  2. Twenty-three sources met inclusion criteria for AI applications, governance or implementation involving marginalised populations or health systems in SSA.
  3. Authors identified two dominant narratives: potential to improve equity versus risk of reinforcing inequities through infrastructure gaps, bias, and weak governance.

The gain

AI applications in healthcare could expand access and improve disease surveillance and health system planning for marginalised populations in Sub-Saharan Africa.

The problem

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.

The 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

  1. Peer-reviewedGlobal Health Action2026-08-03

The debate