The global rise in deaths caused by pulmonary tuberculosis (TB) has placed increased pressure on overburdened healthcare systems to provide TB diagnostic services. Artificial intelligence-based computer-aided diagnosis (AI-based CAD) promises to be a powerful tool in responding to this health challenge by providing actionable outputs which support the diagnostic accuracy and efficiency of clinicians. However, these technologies must first be extensively evaluated to understand their impact and risks before pursuing wide-scale deployment. Yet, health technology assessments for them in real world settings have been limited. Comprehensive evaluation demands consideration of technical safety, human factors, and health impacts to generate robust evidence and understand what is needed for long-term sustainable benefit realisation. This work-in progress study presents a three-stage methodological approach that will be used to guide the data collection and analysis process for evaluating the impact of implementing a commercial AI-based CAD system for TB diagnosis in a real-world radiological setting.