Ultra-portable chest X-ray systems and AI-assisted interpretation tools for tuberculosis screening in India: A...

Tuberculosis (TB) remains a major public health challenge in India, which continues to account for the highest global TB burden. Findings from the National TB Prevalence Survey highlighted a substantial proportion of asymptomatic and subclinical TB cases, emphasizing the limitations of symptom-based screening approaches and the need for more sensitive and accessible screening strategies. Recent advances in ultra-portable chest X-ray systems and artificial intelligence (AI)-assisted chest radiograph interpretation tools have created new opportunities for decentralized and point-of-care TB screening. The objective of the paper is to provide a narrative review of currently available ultra-portable handheld chest X-ray systems and AI-assisted chest radiograph interpretation tools for TB screening in India, with emphasis on their technical characteristics, operational feasibility, reported diagnostic performance, implementation readiness, and programmatic relevance. A narrative review and comparative technology assessment were conducted using information obtained from peer-reviewed literature, World Health Organization guidance documents, national programme reports, publicly available technical specifications, vendor documentation, product brochures, conference materials, and implementation reports. Technologies were reviewed using a structured comparative framework covering technical characteristics, diagnostic performance, operational feasibility, interoperability, regulatory considerations, and implementation feature relevant to decentralized TB screening. Ultra-portable chest X-ray systems demonstrated substantial variability in technical specifications, portability, detector compatibility, battery performance, and operational characteristics. Most systems were battery-operated and compatible with AI-assisted interpretation platforms, supporting their potential use in community-based and resource-constrained settings. AI-assisted chest radiograph interpretation tools demonstrated high reported sensitivity for TB screening, although specificity, validation status, and implementation maturity varied across platforms. Certain tools, including qXR and Genki, reported broader deployment, offline functionality, interoperability with health information systems, and compatibility with cloud- and edge-based workflows. However, publicly available evidence and independent validation data remained limited for several emerging technologies. Ultra-portable chest radiography combined with AI-assisted interpretation represents a promising approach for strengthening decentralized and point-of-care TB screening in high-burden settings. However, considerable heterogeneity exists across available technologies with respect to technical specifications, validation evidence, operational readiness, and implementation maturity. Further independent validation studies, standardized evaluation frameworks, implementation research, and cost-effectiveness analyses are required to guide large-scale integration into national TB screening programmes.