ReviewBMC global and public health2023
Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis.
Review in BMC global and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Accelerating tuberculosis diagnosis in Mozambican prisons using digital chest X-rays with computer-aided detection: a longitudinal, comprehensive health intervention.BMC global and public health · 2026Article
- Bridging the analog divide: a comparison of printed X-ray films and digital images when using computer-aided detection software for tuberculosis screening.BMC global and public health · 2026Article
- Implementing computer‑aided detection for TB active case finding: A qualitative study of feasibility and acceptability in Tondo, Manila.PLOS global public health · 2026Article
- HybridCATNet: an explainable CNN-Transformer framework with early multi-channel fusion and uncertainty estimation for pneumonia detection from chest X-rays.Frontiers in digital health · 2026Article
- Evaluating the Usefulness of Artificial Intelligence-based Chest X-Ray Screening in Improving Tuberculosis Detection Among the High-Risk Tribal Population of Chhattisgarh, India: A Prospective Multi-Centre Study.Open forum infectious diseases · 2026Article
- Review
- Article
- Artificial Intelligence in migrant health: a critical perspective on opportunities and risks.The Lancet regional health. Europe · 2025Review
- Performance of CAD4TB artificial intelligence technology in TB screening programmes among the adult population in South Africa and Lesotho.Journal of clinical tuberculosis and other mycobacterial diseases · 2025Article
- Comparing the accuracy of computer-aided detection (CAD) software and radiologists from multiple countries for tuberculosis detection in chest X-Rays.Scientific reports · 2025Article
- Integration of AI and ML in Tuberculosis (TB) Management: From Diagnosis to Drug Discovery.Diseases (Basel, Switzerland) · 2025Review
- Implementation costs and cost-effectiveness of ultraportable chest X-ray with artificial intelligence in active case finding for tuberculosis in Nigeria.PLOS digital health · 2025Article
- Determining Thresholds for Computer-Aided Detection for Silicosis-An Analytic Approach.American journal of industrial medicine · 2025Article
- Prevalence of Xpert MTB/RIF Ultra Trace Call Results and Associated Risk Factors During Active Tuberculosis Case Finding in Viet Nam: A Programmatic Evaluation.Diagnostics (Basel, Switzerland) · 2025Article
- Comparison of different Lunit INSIGHT CXR software versions when reading chest radiographs for tuberculosis.PLOS digital health · 2025Article
- Undiagnosed burden of latent tuberculosis, active tuberculosis and tuberculosis-HIV co-infections in Africa-status quo, needs, priorities, and opportunities.IJID regions · 2025Review
- Classifying abnormalities in chest radiographs from Vietnam using deep learning for early detection of cardiopulmonary diseases.Frontiers in radiology · 2025Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Global efforts to identify and support people with tuberculosis.BMC global and public health · 2024Article
- Expanding molecular diagnostic coverage for tuberculosis by combining computer-aided chest radiography and sputum specimen pooling: a modeling study from four high-burden countries.BMC global and public health · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite 30 years as a public health emergency, tuberculosis (TB) remains one of the world's deadliest diseases. Most deaths are among persons with TB who are not reached with diagnosis and treatment. Thus, timely screening and accurate detection of TB, particularly using sensitive tools such as chest radiography, is crucial for reducing the global burden of this disease. However, lack of qualified human resources represents a common limiting factor in many high TB-burden countries. Artificial intelligence (AI) has emerged as a powerful complement in many facets of life, including for the interpretation of chest X-ray images. However, while AI may serve as a viable alternative to human radiographers and radiologists, there is a high likelihood that those suffering from TB will not reap the benefits of this technological advance without appropriate, clinically effective use and cost-conscious deployment. The World Health Organization recommended the use of AI for TB screening in 2021, and early adopters of the technology have been using the technology in many ways. In this manuscript, we present a compilation of early user experiences from nine high TB-burden countries focused on practical considerations and best practices related to deployment, threshold and use case selection, and scale-up. While we offer technical and operational guidance on the use of AI for interpreting chest X-ray images for TB detection, our aim remains to maximize the benefit that programs, implementers, and ultimately TB-affected individuals can derive from this innovative technology.
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