AI for Early TB Diagnosis and Drug Resistance Prediction in Morocco
Photo Credit: Feepik
Overview
Tuberculosis remains one of the world's most persistent public health threats, and in Morocco and similar resource-constrained settings, late diagnosis and rising drug resistance continue to cost lives. For the frontline health workers and patients navigating overstretched systems, delayed detection translates directly into poorer outcomes and accelerating transmission.
The project applies AI to two of the most critical gaps in the TB response: early diagnosis and the prediction of drug-resistant Mycobacterium tuberculosis strains. Using machine learning on patient and imaging data, the team is developing AI-enabled tools designed for the realities of low-resource clinical environments supporting faster decision-making without requiring infrastructure that most affected communities do not have. The approach strengthens TB surveillance from the ground up, building systems that can flag resistance patterns before they spread and equip health workers with actionable insights at the point of care.