ABSTRACT
Computer-aided detection algorithms based on artificial intelligence are increasingly being tested and used as a means for detecting tuberculosis in countries where the epidemic is still present. Computer-aided detection tools are often presented as a global solution that can be deployed in all the geographical areas concerned by tuberculosis, but at the same time, they need to be adjusted and calibrated according to local populations' characteristics. The aim of this article is to analyze the tensions between the standardization of computer-aided detection algorithms and their local adaptation and the political issues associated with these tensions. We undertook a qualitative analysis of practices associated with tuberculosis detection algorithms in different contexts, contrasting the perspectives of various stakeholders. Algorithms embed the promise of standardization through automation and the bypassing of variable human expertise such as that of radiologists, they are nonetheless objects of local practices that we have characterized as "tweaking." This work of tweaking reveals how the technology is situated but also the many concerns of the users and workers (insertion in care, control over infrastructure, and political ownership). This should be better considered to truly make computer-aided detection innovative tools for tuberculosis management in global health.
ABSTRACT
Computer Aided Detection software based on Artificial Intelligence (AI-CAD), combined with chest X-rays have recently been promoted as an easy fix for a complex problem: ending TB by 2030. WHO has recommended the use of such imaging devices in 2021 and many partnerships have helped propose benchmark analysis and technology comparisons to facilitate their "market access". Our aim is to examine the socio-political and health issues that stem from using AI-CAD technology in a global health context conceptualized as a set of practice and ideas organizing global intervention "in the life of others". We also question how this technology, which is not yet fully implemented in routine use, may limit or amplify some inequalities in the care of tuberculosis. We describe AI-CAD through Actor-Network-Theory framework to understand the global assemblage and composite activities associated with detection through AI-CAD, and interrogate how the technology itself may consolidate a specific configuration of "global health". We explore the various dimensions of AI-CAD "health effects model": technology design, development, regulation, institutional competition, social interaction and health cultures. On a broader level, AI-CAD represents a new version of global health's accelerationist model centered on "moving and autonomous-presumed technologies". We finally present key aspects in our research which help discuss the theories mobilized: AI-CAD ambivalent insertion in global health, the social lives of its data: from efficacy to markets and AI-CAD human care and maintenance it requires. We reflect on the conditions that will affect AI-CAD use and its promises. In the end, the risk of new detection technologies such as AI-CAD is indeed that the fight against TB could be reduced to one that is purely technical and technological, with neglect to its social determinants and effects.
