aceArtificial Intelligence and Machine Learning-based prediction of tuberculosis treatment failure: A systematic review and meta-analysis
Tuberculosis (TB) remains a leading cause of infectious disease mortality worldwide, and treatment failure contributes to ongoing transmission, drug resistance, and poor clinical outcomes. Artificial intelligence (AI) and machine learning (ML) approaches have attracted growing interest in predicting TB treatment outcomes, but the literature is heterogeneous and lacks a comprehensive synthesis. We systematically searched PubMed/MEDLINE and Embase (January 2000-October 2025) for studies developing or validating AI…
Meta-analysis of 19 studies with 100,790 participants found AI/ML models achieved pooled discrimination of 0.836 for predicting tuberculosis treatment failure.
Most models lacked external validation, only one was low risk of bias, publication bias was detected, and performance dropped in HIV-positive populations, leaving models not ready for routine clinical implementation.
Evidence
- Peer-reviewedPLOS One2026-08-17
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Truvace Impact Record TRV-2026-0817, v1: “Artificial Intelligence and Machine Learning-based prediction of tuberculosis treatment failure: A systematic review and meta-analysis.” Truvace, 2026-08-18. /record/TRV-2026-0817 (accessed at citation time). sha256 114c4d940cee07be…
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