Abstract
The challenges to effective tuberculosis (TB) disease control are considerable, and the current global targets for reductions in disease burden seem unattainable. The combination of complex pathophysiology and technical limitations results in difficulties in achieving consistent, reliable diagnoses, and long treatment regimens imply serious physiological and socioeconomic consequences for patients. Artificial intelligence (AI) applications in healthcare have significantly improved patient care regarding diagnostics, treatment and basic research. However, their success relies on infrastructures prioritising comprehensive data generation and collaborative research environments to foster stakeholder engagement. This viewpoint article briefly outlines the current and potential applications of advanced AI models in global TB control and the considerations and implications of adopting these tools within the public health community.