Abstract

Tuberculosis (TB) remains a significant global health challenge, necessitating advancements in diagnostic technologies to improve early detection and treatment outcomes. This article explores innovative approaches in TB diagnostics, focusing on the integration of molecular techniques and artificial intelligence (AI). Molecular methods, including nucleic acid amplification and sequencing technologies, have enhanced the sensitivity and specificity of TB diagnostics. AI and machine learning algorithms have further revolutionized diagnostic accuracy by analyzing complex datasets and identifying patterns indicative of TB. This review synthesizes recent developments in these fields, evaluates their clinical applications, and identifies future research directions. Our findings highlight the potential of combining molecular techniques with AI to enhance TB diagnosis, reduce diagnostic delays, and improve patient outcomes.