AI-Aided Diagnostics in Malawi's Resource-Constrained Healthcare Settings

AI-aided diagnostics have shown promise in resource-limited healthcare settings by enhancing accuracy and efficiency of disease diagnosis. A cross-sectional study was conducted using machine learning models trained on a dataset of clinical records from Malawian healthcare facilities. The study aimed to assess model accuracy through precision and recall metrics. The AI models achieved an overall accuracy rate of 85% in diagnosing common diseases, with higher precision for malaria cases (90%) compared to tuberculosis (75%). AI-aided diagnostics can significantly improve disease diagnosis outcomes in resource-constrained settings. Further research should focus on model validation across different geographic regions and the integration of AI into existing healthcare workflows. AI, Diagnostics, Malawi, Precision, Recall Model estimation used θ̂=argminθ∑ᵢ(yᵢ,f_θ(xᵢ))+λθ₂², with performance evaluated using out-of-sample error.