Artificial Intelligence (AI) based chest X-ray (CXR) screening for tuberculosis (TB) is becoming increasingly popular. Still, deploying such AI tools can be challenging due to multiple real-life barriers like software installation, workflow integration, network connectivity constraints, limited human resources available to interpret findings, etc. To understand these challenges, PATH implemented a TB REACH active case-finding program in a resource-limited setting of Nagpur in India, where an AI software device (qXR) intended for TB screening using CXR images was used. Eight private CXR laboratories that fulfilled prerequisites for AI software installation were engaged for this program. Key lessons about operational feasibility and accessibility, along with the strategies adopted to overcome these challenges, were learned during this program. This program also helped to screen 10,481 presumptive TB individuals using informal providers based on clinical history. Among them, 2,303 individuals were flagged as presumptive for TB by a radiologist or by AI based on their CXR interpretation. Approximately 15.8% increase in overall TB yield could be attributed to the presence of AI alone because these additional cases were not deemed presumptive for TB by radiologists, but AI was able to identify them. Successful implementation of AI tools like qXR in resource-limited settings in India will require solving real-life implementation challenges for seamless deployment and workflow integration.

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.