Advancements in Artificial Intelligence for the Diagnosis of Multidrug Resistance and Extensively Drug-Resistant Tuberculosis: A Comprehensive Review

Tuberculosis is a global health concern, with MDR-TB and XDR-TB emerging. Traditional methods are time-consuming and inaccurate, leading to treatment delays. Artificial intelligence (AI) has shown promise in diagnosing drug-resistant strains. This review explores AI applications for MDR-TB and XDR-TB diagnosis, including machine learning, deep learning, and ensemble techniques. Challenges include data availability, algorithm interpretability, and regulatory considerations. Future directions include AI integration into clinical practice.