Artificial intelligence is redefining tuberculosis (TB) detection by enabling faster, more accessible diagnostic pathways in remote and underserved regions where healthcare infrastructure remains constrained. By combining advanced analysis of cough acoustics with AI-assisted interpretation of chest X-rays, healthcare systems are strengthening early screening and intervention capabilities.

AI-enabled cough analysis tools use machine-learning models trained on extensive datasets to identify acoustic patterns associated with TB. Deployable through smartphones and portable devices, these tools allow frontline health workers to conduct rapid, non-invasive screening at the community level, reducing reliance on laboratory-intensive diagnostics during initial assessments.

Simultaneously, AI-powered radiology platforms are enhancing diagnostic consistency by supporting the interpretation of chest X-rays in areas facing shortages of trained radiologists. These systems assist clinicians in identifying subtle pulmonary abnormalities, enabling prioritisation of high-risk cases and more efficient referral pathways.

Together, these technologies represent a shift toward decentralised, data-driven healthcare delivery models. By augmenting clinical expertise rather than replacing it, AI solutions are strengthening disease surveillance, improving diagnostic reach, and supporting broader TB elimination efforts. As validation and deployment scale, such innovations are expected to play a vital role in expanding equitable access to quality diagnostics across diverse geographies.