Post-tuberculosis lung disease (PTLD) poses a significant clinical challenge in regions with a high burden of tuberculosis (TB). This review provides a comprehensive overview of PTLD, encompassing its pathogenesis, clinical manifestations, diagnostic modalities, management strategies, long-term outcomes, and public health implications. PTLD arises from residual lung damage following TB treatment and is characterized by a spectrum of pathological changes, including fibrosis, bronchiectasis, and cavitation. Clinical presentation varies widely, from chronic cough and hemoptysis to recurrent respiratory infections, which are oftentimes a diagnostic dilemma. Radiological imaging, pulmonary function tests, and careful consideration of patient history play pivotal roles in diagnosis. Management strategies involve pharmacological interventions to alleviate symptoms and prevent disease progression, which are influenced by the extent of lung damage, comorbidities, and access to healthcare. Rehabilitation programs and surgical options are available for select cases. Prognosis is influenced by the extent of lung damage, comorbidities, and access to healthcare. Prevention efforts through a TB control program and early detection are crucial in reducing the burden of PTLD. This review stresses the importance of understanding and addressing PTLD to mitigate its impact on individuals and public health systems worldwide.

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.