Artificial Intelligence in Tuberculosis Management

Despite medical advances, tuberculosis (TB) diagnosis remains challenging, particularly in resource-limited settings where conventional methods like sputum microscopy and culture can be slow and unreliable. Artificial intelligence (AI)-driven technologies hold the potential to revolutionize TB management by enhancing diagnosis, treatment, and transmission control. AI-based techniques, leveraging advanced algorithms and machine learning (ML), analyze various medical imaging data, including chest X-rays and CT scans, with exceptional precision. By detecting subtle abnormalities indicative of TB, AI significantly improves the diagnostic process, enabling early intervention and containment of the disease. Furthermore, AI facilitates ongoing monitoring of treatment progress and patient compliance, crucial aspects of TB management. Predictive models developed through AI algorithms utilize patient-specific data to personalize treatment regimens and forecast responses, optimizing outcomes. In addition to aiding individual patient care, AI-driven surveillance systems play a vital role in tracking TB epidemiology, including the emergence of drug-resistant strains. By identifying resistance patterns early on, healthcare providers can adapt treatment strategies accordingly, minimizing the development of drugresistant TB strains and improving overall treatment efficacy. This chapter provides an in-depth overview of employing AI for the management of TB, highlighting its potential to revolutionize diagnosis, treatment, and control strategies in combating this global health threat.