Abstract
In 2021, WHO issued a novel recommendation within its tuberculosis screening guidelines: the approval of artificial-intelligence-based computer-aided detection (AI-CAD) to analyse chest x-rays for tuberculosis detection in place of human readers.1 The recommendation was largely based on evidence suggesting that the accuracy of AI-CAD approximates that of radiologists in identifying tuberculosis on chest x-rays.1 Global health donors and actors working to eradicate tuberculosis regard AI-CAD as an important tool for finding the so-called missing millions of people with active tuberculosis that is left undetected each year.2 Donors are also drawn to AI-CAD’s potential for optimising resource allocation by reducing the use of costly confirmatory diagnostics, such as the GeneXpert MTB/RIF assay.3 In the face of the devastating effects of COVID-19 on tuberculosis care and prevention, AI-CAD has been highlighted among the tools that can be used to make the goal of tuberculosis eradication technically and programmatically possible.4 This excitement around AI-CAD for tuberculosis detection has emerged from an evidence base that is nearly singularly focused on estimating accuracy. However, ongoing experiences with implementation of AI-CAD for tuberculosis detection invite the global health community to consider more multifaceted critical assessments. AI-CAD has often been tested in the framework of pilot projects and research partnerships, but these tests have not always led to uptake by state authorities because many considerations have yet to be addressed. In this Comment, we identify and discuss technical, economic, and political considerations surrounding the use of AI-CAD for tuberculosis detection to provide a framework for its implementation in a manner that enhances health equity.