Artificial Intelligence as an Alternative Strategy for the Rapid TB Detection, Discrimination, and Drug‐Resistance Identification: A Systematic Review and Meta‐Analysis

Abstract

ABSTRACT The rising global burden of pulmonary tuberculosis (PTB) and drug‐resistant tuberculosis (TB) necessitates rapid, accurate, and accessible diagnostic tools. This systematic review and meta‐analysis evaluates artificial intelligence (AI) models for TB detection, discrimination from other diseases, and drug‐resistance identification. Following the Preferred Reporting Items for Systematic reviews and Meta‐Analyses (PRISMA) guidelines, we systematically searched four databases (PubMed, Scopus, Web of Science, and Embase) from 2016 to 2025. Following data extraction, study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS‐2) tool. A bivariate random‐effects model was used to estimate pooled sensitivity and specificity. Of the 2740 articles screened, 30 met the inclusion criteria of the quantitative analysis; 16, 6, and 8 articles were conducted on TB detection, discrimination, and drug‐resistance identification, respectively. The pooled sensitivities and specificities were 92% (95% CI 90%–94%) and 79% (95% CI 74%–84%) for TB detection, 87% (95% CI 79%–92%) and 76% (95% CI 66%–86%) for discrimination, and 89% (95% CI 85%–94%) and 94% (95% CI 92%–97%) for drug‐resistance identification. Key limitations include the retrospective design of the majority of studies, dataset heterogeneity, and limited external validation. These findings suggest that AI‐based applications hold significant potential as accurate tools for TB detection, discrimination, and drug‐resistance identification, while simultaneously mitigating reliance on traditional, time‐consuming methods. However, the urgent adoption of standardised reporting guidelines – specifically Standards for the Reporting of Diagnostic Accuracy Studies‐AI for diagnostic accuracy and Consolidated Standards of Reporting Trials‐AI for clinical trials – is essential. Adhering to these frameworks in multicentre studies will be crucial for bridging the gap between cutting‐edge research and the safe, transparent integration of AI into routine clinical practice.

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