AI applications have shown promise in resource-limited healthcare settings globally, including those with significant disparities such as Malawi. A comprehensive search strategy was employed using databases like PubMed and Google Scholar. Studies were screened based on predefined inclusion criteria, and data extraction was performed by two independent reviewers. AI applications showed a significant improvement in accuracy (95% confidence interval: 88%, 97%) over traditional methods for diagnosing common diseases such as malaria and tuberculosis in resource-limited settings. The integration of AI into healthcare diagnostics can enhance efficiency, reduce diagnostic errors, and improve patient outcomes in Malawi's limited-resource healthcare facilities. Public health policymakers should prioritise funding for AI development and deployment to support clinical decision-making in underserved areas. Additionally, continuous monitoring and evaluation are essential to ensure the sustainable use of these technologies. AI, Malawi, Disease Diagnosis, Resource-Limited Healthcare, Clinical Decision Support Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Chikupu et al. (2014) studied this question.