Artificial intelligence-assisted point-of-care lung ultrasound for early detection of pulmonary tuberculosis in children: Implications for nursing practice

Pulmonary tuberculosis (TB) in children remains a significant global health challenge, particularly in low- and middle-income countries, where delayed diagnosis contributes to increased morbidity and mortality. Conventional diagnostic methods for pediatric TB are often limited by invasiveness, radiation exposure, and low diagnostic sensitivity. Point-of-care lung ultrasound (POCUS-LUS) has emerged as a promising, radiation-free imaging modality that is well-suited for pediatric populations. Recent advances in artificial intelligence (AI) have further enhanced the diagnostic potential of lung ultrasound through automated image interpretation and clinical decision support. This review examines the role of AI-assisted POCUS-LUS in the early detection of pediatric pulmonary tuberculosis, with a particular focus on implications for nursing practice. Key areas discussed include current diagnostic challenges, integration of AI into lung ultrasound, the expanding role of nurses in TB screening, ethical and professional considerations, and future directions for nursing education and health policy.