The Role of AI in Tuberculosis Diagnosis: An Umbrella Review

Abstract

Tuberculosis remains a major global health challenge, and the World Health Organization has endorsed artificial intelligence tools to support imaging-based screening. This umbrella review mapped AI applications across tuberculosis prevention, diagnosis, and treatment, focusing on data modalities, algorithm types, and validation practices. Following PRISMA guidelines, systematic reviews published between January 2020 and June 2025 were identified through PubMed and Web of Science, and eligible studies were screened, extracted, and appraised in duplicate. Twelve reviews covering 648 primary studies were included from an initial 1,796 records. Accuracy, AUC, sensitivity, and specificity were reported in 54 %, 43 %, 45 %, and 40 % of studies, respectively. Only 33 % conducted internal validation and 4 % performed external validation against independent cohorts or human readers. Deep learning models such as VGG16, ResNet50, and InceptionV3 dominated, while classical algorithms like support vector machines and random forests persisted in contexts requiring interpretability or when data were limited. The findings highlight rapid growth in AI use for tuberculosis but reveal inconsistent reporting, limited external validation, and minimal attention to missing data. Broader clinical adoption will require rigorous, multimodal studies with standardized performance metrics, transparent algorithms, and robust validation strategies.