Scoping Review on AI in Healthcare for Low-Resource Settings, 2000–2025
by Areeba Shahid·Updated 2mo ago
18.1 KB1files
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Description
A 2026 scoping review by Areeba Shahid analyzes 60 sources from 2000 to 2025 on ethical, regulatory, and implementation barriers to AI in healthcare for low- and middle-income countries. The review, following PRISMA-ScR guidelines, maps literature from PubMed, Scopus, and Cochrane Library alongside global health policy reports. It reports that 7.4% of LMICs have national AI strategies and over 60% of AI models rely on non-representative datasets.
Use Cases
Analyzing governance and regulatory gaps for AI in healthcare based on the review's findings from WHO and OECD frameworks.
Identifying workforce readiness and training needs based on the reported statistic that fewer than 10% of institutions offer structured AI training.
Studying contextual bias in AI models based on the finding that over 60% of models in LMICs use non-representative datasets.
Comparing implementation strategies across countries based on case studies from Brazil and India mentioned in the results.
Strengths
Analysis is based on 60 sources, including 25 on ethics, 17 on regulatory gaps, and 18 on implementation.
Provides specific statistics, including that 7.4% of LMICs have adopted national AI strategies.
Follows a structured methodology using PRISMA-ScR guidelines and the PCC framework.
Limitations
Row count is unknown, which may limit suitability assessment.
Column-level documentation is absent; field semantics must be inferred after download.
The dataset is a 18.1 KB DOCX file, indicating a limited scope focused on review text rather than primary data.
Provenance
Source
Areeba Shahid via figshare.
Collection Method
Systematic mapping of literature using PubMed, Scopus, and Cochrane Library (2000–2025) and global health policy reports, following PRISMA-ScR guidelines.
Time Range
2000–2025
Freshness
Last updated 2026-04-13 05:47:44; freshness should be verified.
Geography
Low- and middle-income countries (LMICs), with case studies from Brazil and India.
File is in DOCX format; content is a review document, not a structured data table.