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Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation.

Hien Thi Thu Nguyen1,2,3, Vang Le-Quy4,5, Anh Tuan Dinh-Xuan6

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Public Health

Background:

  • Artificial intelligence (AI) is increasingly utilized for tuberculosis (TB) screening and diagnosis.
  • Computer-aided detection (CAD) applied to chest radiography (CXR) is a prominent AI application in TB diagnostics.

Purpose of the Study:

  • To conduct a state-of-the-art review of AI applications across the TB diagnosis pathway.
  • To synthesize evidence on the programmatic value, diagnostic accuracy, and implementation of AI in TB care.

Main Methods:

  • A narrative review synthesizing evidence from WHO policy documents, validation initiatives, and peer-reviewed studies (2010-2026).
  • Selection process aligned with PRISMA principles for structured evidence synthesis.

Main Results:

  • Computer-aided detection for chest radiography (CAD-CXR) is WHO-recommended for TB screening in specific contexts, showing comparable sensitivity to human readers.
  • Implementation studies indicate improved screening efficiency and potential cost-effectiveness in high-burden settings.
  • Other AI modalities show promise but require further validation; impact hinges on health system integration and patient-level outcomes.

Conclusions:

  • AI holds significant potential to strengthen TB screening and diagnostic pathways.
  • A differentiated approach is crucial: responsible scale-up of endorsed tools and rigorous evaluation of emerging technologies.
  • Effective and equitable TB care depends on AI integration, considering programmatic value beyond diagnostic accuracy alone.