Is the public sector Africa’s hidden force for AI-driven healthcare transformation?
Bibliographic record
Abstract
The transformative impact of Artificial Intelligence (AI) in today’s interconnected world is extensive, reshaping capabilities, enhancing efficiencies, and creating pathways to address critical global challenges. AI demonstrates potential in healthcare for improving outcomes through enhanced diagnostics, predictive analytics, and personalized treatment plans, directly contributing to Sustainable Development Goal 3 (SDG 3) on good health and well-being. However, the benefits of AI remain unevenly distributed, with notable gaps in adoption and access, particularly in lower-resource regions like Africa. This paper conducts a comprehensive scoping review to examine the role of the public sector in advancing AI integration within African healthcare systems. The key contributions include: (1) synthesizing existing research to identify trends, gaps, and progress in AI adoption, (2) highlighting practical examples of AI applications that show promise in improving healthcare delivery, (3) analyzing the major barriers to widespread adoption, and (4) outlining policy implications and actionable recommendations. The discussion is framed around six core components of a sustainable AI ecosystem: skills development, data access, computational resources, supportive policy environments, financing, and multi-sector partnerships. The findings suggest that with coordinated public sector leadership, strategic investment, and effective policy implementation, AI can improve healthcare outcomes and support sustainable development across the continent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".