On the use of synthetic data for healthcare AI in Africa: Technical performance, governance challenges, and policy readiness

Abstract

Purpose: Synthetic data has emerged as a promising solution to overcome the shortage of clinical datasets needed for training healthcare artificial intelligence (AI) models. This study examined how synthetic data can support AI development in Africa's healthcare by analyzing its technical performance, fidelity limitations, and governance implications within low-resource health systems. Methods: A Critical Literature Review was conducted on scholarly and technical literature focused on the use of synthetic data for AI in healthcare across African settings. Databases searched included Scopus, Web of Science, PubMed, and Google Scholar. Thematic analysis identified trends in synthetic data generation, fidelity, domain adaptation, and adoption challenges in African healthcare AI. Results: Drawing on interdisciplinary evidence, the analysis demonstrates how addressing technical challenges, improving synthetic data fidelity, leveraging domain adaptation techniques, and confronting practical adoption barriers are critical to enhancing the reliability and applicability of synthetic data for AI-driven healthcare in Africa. Four themes emerged from the analysis. First, hybrid synthetic-real datasets consistently outperform synthetic-only models. Second, fidelity gaps introduced bias risk and misclassification. Third, domain adaptation remains underused in low-resource contexts. Fourth, infrastructure gaps, weak regulation, and clinician skepticism hindered the adoption of synthetic data. Conclusion: Synthetic data can enhance AI-enabled healthcare in Africa if it is embedded within regulatory frameworks, validated through hybrid modeling, and supported by investment in infrastructure and capacity-building. This study highlights the intersection of synthetic data, healthcare AI, data fidelity, domain adaptation, and governance considerations in African health systems, underscoring the need for robust health technology assessment processes.