Tuberculosis (TB) remains a significant global health concern, particularly with the emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). Traditional methods for diagnosing drug resistance in TB are time-consuming and often lack accuracy, leading to delays in appropriate treatment initiation and exacerbating the spread of drug-resistant strains. In recent years, artificial intelligence (AI) techniques have shown promise in revolutionizing TB diagnosis, offering rapid and accurate identification of drug-resistant strains. This comprehensive review explores the latest advancements in AI applications for the diagnosis of MDR-TB and XDR-TB. We discuss the various AI algorithms and methodologies employed, including machine learning, deep learning, and ensemble techniques, and their comparative performances in TB diagnosis. Furthermore, we examine the integration of AI with novel diagnostic modalities such as whole-genome sequencing, molecular assays, and radiological imaging, enhancing the accuracy and efficiency of TB diagnosis. Challenges and limitations surrounding the implementation of AI in TB diagnosis, such as data availability, algorithm interpretability, and regulatory considerations, are also addressed. Finally, we highlight future directions and opportunities for the integration of AI into routine clinical practice for combating drug-resistant TB, ultimately contributing to improved patient outcomes and enhanced global TB control efforts.

Tuberculosis (TB) remains a major global public health concern, which has become worse in low- and middle-income nations due to the rise of drug-resistant strains of the disease. Bedaquiline, a groundbreaking anti-TB drug, shows great promise for addressing multidrug-resistant TB (MDR-TB). However, the recent decision by India to reject the application for a patent extension on bedaquiline raises crucial questions regarding access to essential medicines and public health requirements. This article examines the significance of India\'s rejection of the patent extension for bedaquiline, outlining the global implications of this decision and its impact on intellectual property rights, access to medicines, and the future of drug development. India\'s stance serves as a model for other countries to prioritize public health in their patent-related decisions, underlining the need for a balanced approach to intellectual property rights, innovation, and affordability in the pharmaceutical sector. In the context of the ongoing battle against drug-resistant TB, India\'s decision on bedaquiline signifies the evolving landscape of pharmaceutical patent practices in the service of global public health.