Tuberculosis in Low- and Middle-income Countries: A Critical Analysis of Health System Barriers and the Promise of Artificial Intelligence

The burden of Tuberculosis (TB) is disproportionately high in low- and middle-income countries (LMICs), where structural health-system constraints and social disparities delay diagnoses and undermine treatment outcomes. This review analyzed evidence across five high-burden LMICs (India, Indonesia, Nigeria, the Philippines, Pakistan). It appraised the emerging role of Artificial Intelligence (AI) in TB diagnosis, monitoring treatment adherence, and assessing outcomes.

The review followed PRISMA guidelines and conducted a Johns Hopkins Evidence-Based Practice appraisal of peer-reviewed studies, national program reports, and selected grey literature (January 2015–December 2025). Published data on epidemiology, health financing, service integration, diagnostics, and digital/AI-powered interventions were analyzed and summarized into descriptive themes. Classic TB publications date back to 2005.

India had the largest absolute TB and multidrug-resistant TB (MDR/RR-TB) burden, while Nigeria recorded the highest mortality and TB/HIV co-infection prevalence. Case detection rates ranged from ~64% to 79%, and treatment success rates ranged from ~74% to 86% across countries. Domestic funding for TB control programs accounted for 80% or more of national TB budgets in India. Nigeria, Pakistan, and the Philippines reported that less than 50% of their TB control programs' funding came from local sources, thereby relying on donor funding to cover budget deficits. AI-driven chest radiography and technology for monitoring TB treatment adherence and outcomes showed promise. Researchers in pilot studies found that AI-assisted TB diagnoses, treatment access, and outcome monitoring were hindered by poor infrastructure, inadequate and untrained TB healthcare staffing, scalability, and sustainability constraints. Additionally, social disparities, environmental factors, and stigma adversely affect early diagnosis and management of TB among rural dwellers, women, children, and hard-to-reach communities.

National TB funding deficits and fragmented national health systems and insurance schemes resulted in weak surveillance and poor TB/HIV care integration treatment outcomes. Although AI applications can augment TB screening and monitoring of treatment adherence and outcomes, scalability, equity, and the ethical and transparent use of AI technology were hampered mainly by funding deficits and poor digital infrastructure. Research validating the impact of AI on national TB programs was primarily conducted through pilot investigations.

Progress toward End TB targets will require robust domestic funding, integration of TB control programs into primary and national insurance schemes, and a phased, scalable, and context-appropriate approach to AI deployment. TB control programs in LMICs will benefit from incorporating AI tools within broader health-system reforms to ensure equitable, sustainable TB control.