Regulating AI: A Comprehensive Review of Strategies for the Ethical and Safe Use

Malaria remains a major public health challenge in the Democratic Republic of Congo (DRC), particularly in Lubumbashi, where traditional diagnostic methods are struggling to meet growing demand.The study was conducted at the University Clinics of Lubumbashi (UCL), the teaching hospital affiliated with the University of Lubumbashi.This work proposes an expert system based on artificial intelligence (AI) and the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to optimize malaria diagnosis in this setting.By leveraging a decision tree classifier trained on local clinical data, the system achieved an accuracy of 90.4%, a recall of 88%, and a specificity of 92%.The results demonstrate a substantial improvement in the speed and reliability of diagnosis, providing a transparent and interpretable decision-support tool suitable for resource-limited healthcare environments.