Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation

Background: Artificial intelligence (AI) is increasingly used to support tuberculosis (TB) screening and diagnosis, especially computer-aided detection (CAD) applied to chest radiography (CXR). The value of these programs depends not only on diagnostic accuracy but also on threshold calibration, integration into clinical workflow, and capacity for confirmatory testing. Methods: We conducted a narrative state-of-the-art review of AI applications relevant to TB screening and diagnosis. We synthesize evidence from World Health Organization policy documents, independent validation initiatives, and peer-reviewed studies re-porting diagnostic performance and real-world implementation outcomes. Results: CAD for CXR is the most mature AI application and is recommended by WHO for TB screening and triage among individuals aged ≥15 years in specific contexts. CAD-CXR can achieve sensitivity comparable to human readers, although performance varies by product, software version, population, and imaging conditions. Threshold selection is therefore a programmatic decision influencing referral volume and resource use. Inde-pendent benchmarking and local verification studies are essential to confirm performance and assess subgroup variability, including among people living with HIV and those with prior TB. Other AI approaches, including computed tomography (CT)-based imaging analysis, point-of-care ultrasound interpretation, cough or stethoscope sound analysis, clinical risk models, and genomic resistance prediction, are still at earlier stages and generally require further independent validation before routine programmatic use. Conclusions: AI has the potential to strengthen TB screening and diagnostic pathways, but impact should be evaluated using patient- and program-level outcomes rather than accuracy alone. Responsible scale-up requires local calibration, governance safeguards, and ongoing monitoring in real-world settings.