Beyond overall accuracy: subgroup performance and threshold calibration of AI-assisted chest radiography for equity-oriented pulmonary tuberculosis screening

Artificial intelligence-assisted computer-aided detection (CAD) can expand access to chest radiography interpretation for pulmonary tuberculosis screening and triage, but high overall discrimination does not ensure safety at a deployed threshold. This focused narrative Mini Review synthesizes evidence on subgroup performance, threshold calibration, external validity, and implementation of CAD for adult pulmonary tuberculosis. Smear-negative, asymptomatic, and some HIV-associated disease may be less radiographically visible, increasing false-negative risk. Conversely, previous tuberculosis and competing chronic abnormalities reduce specificity and concentrate unnecessary confirmatory testing. Sex, age, geography, clinical setting, verification design, and software version can further alter operating performance. A numerical threshold is therefore not a portable algorithm property but a context-specific clinical resource-allocation policy. We propose a lifecycle in which programs define the intended use, lock the product and version, conduct representative local validation, select an operating point under explicit sensitivity and capacity constraints, audit clinically relevant subgroups, and monitor the complete care pathway. Stratified thresholds and multivariable models may improve selected settings, but current evidence does not justify replacing robust local calibration and governance. Prospective evaluations should compare threshold strategies under common verification standards and report missed cases, referral burden, treatment initiation, and patient-important outcomes.