The Use of Artificial Intelligence in Tuberculosis Care: A Scoping Review

Abstract

Tuberculosis (TB) is a subacute to chronic respiratory infection with insidious onset and protean symptoms. Treatment is complex and requires a multi-drug, multi-month regimen in which adherence is critical. Artificial intelligence (AI) offers promising solutions to challenges across the TB care cascade including screening, diagnosis and treatment. We conduct a scoping review of the literature published from 2017 to 2025 on the use of AI in TB screening, diagnosis, drug resistance diagnosis, treatment monitoring, and regimen design. We then extract data on study characteristics, AI methodology, input data, and sample size and describe AI tool performance and technology readiness level (TRL). Ninety studies are included, representing 803,383 study participants across 24 countries. Most studies (n=46) focus on radiological imaging for TB screening or diagnosis, but a burgeoning number of studies address drug resistance diagnosis (n=11), regimen design (n=4), treatment monitoring (n=12), and treatment adherence (n=8). Reported accuracy of AI interpretation of chest imaging for TB diagnosis was high at a median Area Under the receiver operating Curve (AUC) of 0.94 [IQR 0.12, range 0.81-0.99] for internal validation and 0.89 [IQR 0.14, range 0.66-0.98] for external validation, and a median TRL of 5 (IQR 1, range 4-7). AI demonstrates promise for advancing TB care towards World Health Organization (WHO) End TB targets, but several gaps remain, including AI-ready fit-for-task data availability, limited external validation and challenges in clinical integration. Closing these gaps will be critical for realizing the full potential of AI in TB care towards WHO End TB targets.

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