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Artificial intelligence (AI) is rapidly transforming tuberculosis (TB) diagnosis. It is addressing the longstanding challenges in accuracy, efficiency, and accessibility. Traditional diagnostic methods, while effective, often suffer from limitations such as variability in sensitivity and lengthy turnaround times. AI technologies, including machine learning and deep learning algorithms, offer innovative solutions by automating the analysis of chest X-rays, genomic data, and clinical parameters. These advancements promise improved diagnostic accuracy, expedited treatment initiation, and personalized medicine approaches. However, successful implementation requires overcoming challenges related to data quality, integration with healthcare systems, and ethical considerations. Moving forward, this paper sheds light on AI-driven TB diagnosis, which stands poised to enhance global healthcare outcomes through enhanced detection capabilities and optimized treatment strategies.
Yadav et al. (2024) studied this question.