Abstract
Abstract
A robust healthcare system is fundamental to India's vision of becoming a developed nation by 2047 and harnessing its demographic dividend for a sustained economic growth. However, Tuberculosis (TB) remains India's most critical public health challenge, with the country accounting the highest share of the global TB burden at 25–30% of cases, impeding India’s sustainable growth. TB is not merely an outcome of bacterial infection—it is deeply embedded in social determinants such as poverty, malnutrition, migration, and unequal access to healthcare, rendering conventional TB elimination strategies inadequate in defeating the disease. Despite India's commitment to eliminate TB by 2025, five years ahead of the 2030 Agenda for Sustainable Development (SDG 2030), the goal remains unmet. Through thematic analysis of India's TB-related policies and programmes, the present paper attempts to expose the legislative and structural governance gaps that perpetuate fragmented and decentralised TB management. The present paper explores this fragmented reactionary approach towards TB as the primary impediment in India’s TB elimination targets. Therefore, this paper proposes the exigency for a centralised AI-enabled healthcare governance model at various centre and state levels for achieving India’s national TB elimination resolve by integrating Artificial Intelligence across screening, diagnosis, treatment monitoring, and benefit delivery within a proactive framework to India's socio-economic heterogeneity.