Background: Suboptimal adherence to tuberculosis (TB) treatment continues to undermine global TB control efforts, particularly in low- and middle-income countries. In recent years, machine learning (ML) approaches have been explored as tools to predict adherence-related outcomes and guide targeted interventions. Objective: The objective of this study was to systematically review and synthesize the predictive performance of ML models for TB treatment adherence, nonadherence, and loss to follow-up. Methods: We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Multiple databases were searched from inception to March 2026. Studies that used ML models to predict TB adherence-related outcomes and reported the area under the receiver operating characteristic curve (AUC) were included. We performed a random-effects meta-analysis of logit-transformed AUC values, along with assessments of heterogeneity, publication bias, sensitivity analysis, and risk of bias using the Prediction model Risk of Bias Assessment Tool. Results: Seven studies met inclusion criteria. The reported AUC values ranged from 0.72 to 0.95. The pooled AUC was 0.84 (95% confidence interval: 0.76–0.90), indicating good predictive performance. Substantial heterogeneity was observed ( I 2 = 99.9%, P < 0.001). Sensitivity analysis demonstrated stability of results. Egger’s test showed no evidence of publication bias ( P = 0.915). Most studies were judged to be at high risk of bias, particularly in the analysis domain. Conclusion: ML models demonstrate promising ability to predict TB treatment adherence-related outcomes. However, variability in study methods and a high risk of bias underscore the need for standardized approaches and rigorous external validation.
No takes yet. Share an insight, caveat, or question.
Kumar et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: