AI diagnostic applications are increasingly being explored in resource-constrained healthcare settings to address the challenges of limited medical personnel and equipment. A systematic review was conducted using relevant literature from to present. AI applications have shown promise with a significant proportion (45%) of reviewed studies reporting improved diagnostic accuracy compared to traditional methods. While AI shows potential, further empirical research is needed to validate its long-term efficacy in resource-limited settings. Investment should be directed towards training healthcare workers and infrastructure improvements alongside the integration of AI technologies. AI diagnostics, disease diagnosis, Malawi, resource-limited healthcare, machine learning Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
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Salum Chitika
Lilongwe University of Agriculture and Natural Resources
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Salum Chitika (Tue,) studied this question.
www.synapsesocial.com/papers/699d401ade8e28729cf6522d — DOI: https://doi.org/10.5281/zenodo.18733607