Artificial Intelligence (AI) applications have shown promise in improving healthcare outcomes globally, particularly in resource-limited settings where traditional diagnostic methods are insufficient. A mixed-methods approach was employed, including surveys of healthcare professionals in resource-constrained settings, observational studies at selected clinics, and machine learning models developed using available clinical data. AI diagnostic tools demonstrated an accuracy rate of 85% in identifying common diseases such as malaria and tuberculosis compared to traditional methods. However, there was a significant variability in tool performance across different types of facilities. While AI shows potential for enhancing disease diagnosis in resource-limited settings, further research is needed to address issues related to tool adaptation and local healthcare infrastructure challenges. Investment should be directed towards training healthcare workers on AI diagnostics and developing localized versions of AI tools that can operate with minimal technical support. AI, Disease Diagnosis, Resource-Limited Healthcare, Malawi Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.
Musa et al. (2001) studied this question.