Comprehensive Summary
The study explores the use of AI-based Computer-Aided Detection (AI-CAD) software for tuberculosis (TB) detection, focusing on chest X-rays as a detection method. Supported by the World Health Organization, this technology aims to end TB by 2030. The research employs Actor-Network Theory to analyze how AI-CAD reorganizes global health networks and whether it prioritizes technical solutions over addressing social determinants of TB. While AI-CAD is promising, especially in resource-limited settings, it may obscure critical social, political, and health system issues. The study highlights the risk of reducing the fight against TB to a purely technical endeavor, potentially overlooking systemic factors like healthcare access and societal inequalities.
Outcomes and Implications
AI-CAD offers significant potential for improving TB detection, particularly in areas with limited healthcare resources. Its ability to automate and enhance diagnostic processes can lead to more efficient TB management. However, the technology's focus on technical efficiency may inadvertently neglect broader systemic issues, such as healthcare access and social inequalities. For AI-CAD to be truly effective in the global fight against TB, it must be integrated into comprehensive health strategies that address these underlying factors. The study underscores the importance of balancing technological advancements with efforts to improve healthcare infrastructure and address social determinants of health.