Artificial Intelligence in Tuberculosis Control in India: A Review

Tuberculosis (TB) is a chronic infectious disease caused by the bacterium Mycobacterium Tuberculosis, which most commonly affects the lungs but can also impact other parts of the body such as the kidneys, spine, and brain. India continues to carry one of the highest burdens of tuberculosis (TB) globally, necessitating scalable, rapid, and accurate diagnostic and prognostic tools. This literature review examines applications of artificial intelligence (AI) particularly machine learning (ML) and computer-aided detection (CAD) in TB screening, diagnosis, and treatment adherence within India. It assess AI-driven chest X-ray interpretation systems like qXR, Genki, DeepTek, DxTB, large-scale model validations, adherence prediction frameworks, and pilot deployments like mobile vans, slum-based screening. Key challenges and research gaps are identified, including dataset diversity, real-world integration, fairness, and cost-effectiveness in resource-limited settings. The review synthesizes lessons from contemporary initiatives like Wadhwani AI and mobile diagnostic units under the NTEP. This study highlights the landscape of AI driven tuberculosis screening. Concluding remarks focus on the identification of research gap and suggests strategic pathway for long term, sustainable integration of AI into TB elimination programme of our country.