AI-driven diagnostics improve tuberculosis detection in remote areas.
Artificial intelligence (AI) is helping improve the detection of tuberculosis (TB) by making screening faster and more accessible in remote and underserved regions. In areas where healthcare infrastructure and specialist availability are limited, AI tools are supporting earlier identification of potential TB cases and more timely follow-up.
One approach involves AI-based analysis of cough sounds. Using machine-learning models trained on large datasets, these tools can identify patterns in cough recordings that may indicate TB. Because they can be used through smartphones or portable devices, frontline health workers are able to carry out quick, non-invasive screening within communities, reducing the need for immediate laboratory testing at the initial stage.
AI is also being used to support the interpretation of chest X-rays. In regions with limited access to trained radiologists, AI-powered imaging systems help clinicians detect signs of lung abnormalities associated with TB. This assistance improves consistency in readings and helps prioritise patients who require further evaluation or referral.
Together, these technologies are contributing to a more decentralised approach to healthcare delivery, bringing diagnostic support closer to patients. Rather than replacing clinical judgement, AI tools are designed to assist healthcare workers and improve decision-making. As these systems continue to be tested and deployed more widely, they are expected to strengthen TB screening efforts and expand access to quality diagnostics, particularly in areas that face ongoing healthcare resource constraints.