Comprehensive Summary
The World Health Organization and other international health partnerships have supported the promotion of AI-based Computer-Aided Detection (AI-CAD) software for tuberculosis (TB) detection using chest X-rays as a potential solution to help end TB by 2030. However, there are significant health and sociopolitical concerns surrounding this technology, particularly regarding how it may perpetuate disparities in TB treatment and access. Using Actor-Network Theory, the study examines how AI-CAD reorganizes global health networks and whether such technologies prioritize technical fixes over addressing the underlying social determinants of tuberculosis.
Outcomes and Implications
Although AI-CAD presents a promising tool for TB detection, especially in low-resource settings, its deployment risks obscuring critical social, political, and systemic health concerns. The technology’s incorporation into global health strategies may place excessive emphasis on efficiency and automation, potentially neglecting broader issues such as healthcare access, poverty, and inequality. While AI-CAD offers valuable opportunities for improving diagnostic capacity, the study warns that an overreliance on technological solutions could reduce the global fight against TB to a purely technical challenge, rather than a comprehensive public health effort.