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TruaceTracing the truth around AIMonday, July 13, 2026
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Policy·P Space·Evidence-backed problem·Published 2026-07-13

Navigating ethical, regulatory, and implementation barriers to AI in healthcare: pathways toward inclusive digital health in low-resource settings—a scoping review

Background: Artificial intelligence (AI) has the potential to revolutionize healthcare delivery in low- and middle-income countries (LMICs), yet its rapid adoption raises complex ethical, regulatory, and implementation challenges. This review investigates these barriers and identifies emerging strategies that support equitable and inclusive AI deployment in resource-limited settings. Methods: Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines, a systematic mapping of literature was conduc…

TRV-2026-0194Peer-reviewedPermanent record — cite & verify
Navigating ethical, regulatory, and implementation barriers to AI in healthcare: pathways toward inclusive digital health in low-resource settings—a scoping review

Number of published AI governance guideline documents by country by Authors of the study: Nicholas Kluge Corrêa Camila Galvão James William Santos Carolina Del Pino Edson Pontes Pinto Camila Barbosa Diogo Massmann Rodrigo Mambrini Luiza Galvão Edmund Terem Nythamar de Oliveira. CC BY 4.0 · https://creativecommons.org/licenses/by/4.0

The quick read

Published April 13, 2026, this scoping review mapped literature on AI in healthcare in low- and middle-income countries, screening sources from 2000-2025 and including 60 studies that addressed ethical, regulatory, or implementation issues.

It matters because it quantifies how limited representative data, national strategies, and training constrain equitable deployment, while also showing that evidence on successful operationalization remains sparse, leaving uncertainty about which participatory governance and capacity-building models work at scale.

Main points
  • Review of 60 sources from 2000-2025 found 25 focused on ethics, 17 on regulatory gaps, and 18 on implementation barriers to AI in LMICs.
  • Only 7.4% of LMICs have adopted national AI strategies, with workforce gaps as fewer than 10% of institutions offer structured AI training.
  • Case studies from Brazil and India were cited as examples of context-sensitive design to address barriers.
Problem

In low- and middle-income countries, AI for healthcare faces systemic barriers including contextual bias from non-representative datasets and low governance and workforce readiness.

The rundown

The authors conducted a PRISMA-ScR scoping review of PubMed, Scopus, Cochrane Library and policy reports from 2000-2025, analyzing 60 sources across governance, privacy, and AI applications using WHO and OECD frameworks.

Results quantified gaps: 7.4% strategy adoption, over 60% reliance on non-representative data, and fewer than 10% of institutions with structured AI training, with Brazil and India case studies illustrating context-sensitive approaches.

Sources

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