Systematic literature review assesses AI diagnostic accuracy for tuberculosis in Tanzania, indicating need for further validation.
The prevalence of tuberculosis (TB) in Tanzania is significant, necessitating effective diagnostic tools to improve patient outcomes. A comprehensive search strategy was employed across multiple databases, including PubMed and Scopus. Studies were screened based on predefined eligibility criteria. AI diagnostic tools showed moderate accuracy (mean AUC = 0.75 ± 0.10) in detecting TB cases. Existing AI-based TB diagnostics have shown promise but require further validation and refinement for widespread clinical use. Further research should focus on developing culturally sensitive models and integrating them into current healthcare systems.
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Mwakwayinda et al. (2013) studied this question.
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