BMC Global and Public Health is calling for submissions to our Collection on innovative approaches to addressing pressing health challenges in resource-limited settings. This initiative aims to bring together cutting-edge research and interdisciplinary insights to advance global health equity and improve outcomes for vulnerable populations. This Collection specifically seeks to explore the transformative potential of artificial intelligence (AI) in reshaping strategies for tuberculosis (TB) diagnosis in community- and facility-based active case finding. It invites studies leveraging AI tools, particularly those applied to chest X-ray (CXR) analysis, to enhance the accuracy, speed, and accessibility of TB screening and confirmatory rapid molecular diagnostics. We welcome research on the integration of AI technologies in public health systems, assessments of their impact on TB care pathways, and investigations of ethical, societal, and operational challenges.
The Collection’s goals include:
- Advancing evidence on the use of AI for improving the efficiency and reliability of TB screening methods.
- Exploring the role of AI-enhanced CXR analysis in active case finding efforts across diverse populations and varieties of settings.
- Evaluating the cost-effectiveness and scalability of AI solutions for TB diagnosis in low- and middle-income countries.
- Addressing ethical and equity considerations in the deployment of AI for global health initiatives.
- Assessing psychological, commercial, and regulatory enablers and barriers in market access and supply chain, data protection and security, acceptance/hesitancy and uptake, pricing, and affordability of AI for TB.
- Proposing frameworks for integrating AI-driven screening tools into existing healthcare infrastructures.
By fostering a multidisciplinary dialogue, this Collection aims to define pathways for responsibly harnessing AI technologies in TB care and prevention efforts.
We encourage work from local, regional, national, and global partnerships and collaboration among multidisciplinary scientists using multiple methodologies. We ask that authors use non-stigmatizing/patient-centered language as outlined in relevant language guidelines for their respective fields.
This Collection supports and amplifies research related to SDG 3: Good Health and Well-being and SDG 10: Reduced Inequalities.
All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.
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