# Artificial intelligence-assisted chest X-ray for tuberculosis case finding in low- and middle-income countries: implementation experiences and impact

## TL;DR

AI helps detect tuberculosis in chest X-rays, improving diagnosis in low- and middle-income countries.

## Contribution

AI-assisted chest X-ray workflows improve TB case detection and streamline diagnosis in resource-limited settings.

## Key findings

- AI improves diagnostic accuracy and reduces turnaround time for TB detection.
- AI enables efficient parallel diagnosis of multiple chest disorders in low-resource settings.
- AI integration supports decentralized care delivery and early disease diagnosis.

## Abstract

Medical imaging is fundamental to healthcare systems, aiding in the detection of internal abnormalities related to medical conditions beyond their physical symptoms. In low- and middle-income countries (LMICs), limited access to advanced imaging and scarcity of radiologists for image interpretation are evident. Upgrading available resources with artificial intelligence can expand the diagnostic capacity of LMICs to manage the growing prevalence and incidence of infectious diseases such as tuberculosis (TB). Chest X-rays can act as an effective triage tool for TB screening, and multiple models have been reported to improve the number of cases detected in high-burden settings. The case-finding strategies reported in literature have also demonstrated improved diagnostic accuracy and turnaround time post adoption of artificial intelligence (AI) for chest X-ray interpretation. AI assistance can help in identifying radiological involvement of TB, irrespective of their clinical symptoms. Furthermore, cost-effective, integrated workflows can also efficiently support LMICs by facilitating parallel diagnosis and appropriate linkage to care for multiple chest disorders through a unified pathway, thereby broadening the capabilities of chest X-ray based TB screening. By optimizing and strengthening LMIC health systems with AI, further scale-up and implementation can foster a supportive ecosystem for early disease diagnosis and decentralized care delivery.