AI Techniques for Diagnostics in Malawi's Limited Healthcare Contexts

Abstract

AI techniques are increasingly being explored for diagnostics in resource-limited healthcare settings, particularly in sub-Saharan Africa where access to trained professionals is often scarce. The study employs machine learning algorithms, specifically a logistic regression model, to analyse clinical data. Uncertainty in predictions is quantified through robust standard errors. The AI model achieved an accuracy rate of 85% in diagnosing common diseases such as malaria and tuberculosis, with certain demographic groups showing higher diagnostic consistency. Despite initial promising results, further validation and ethical considerations are required before widespread implementation. Future research should focus on broader clinical applications and ensure model transparency to address potential mistrust among healthcare providers and patients. AI diagnostics, Malawi, logistic regression, resource-limited settings Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.

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