Artificial intelligence and artificial immune systems: transforming tuberculosis diagnosis
DOI:
https://doi.org/10.18011/bioeng.2025.v19.1272Keywords:
Medical Innovation, Healthcare Efficiency, Negative Selection Algorithm, Disease DetectionAbstract
Tuberculosis (TB) is one of the world’s deadliest infectious diseases, making rapid and accurate diagnosis essential for its control. However, challenges such as a lack of infrastructure and qualified professionals hinder detection, especially in low- and middle-income countries. In this scenario, Artificial Intelligence (AI) and Artificial Immune Systems (AIS) emerge as innovative tools to enhance TB diagnosis. AI has been applied to the analysis of chest X-rays and molecular tests, increasing accuracy and reducing diagnosis time. Deep learning algorithms can identify subtle patterns in medical exams, achieving accuracy levels comparable to those of specialists. Meanwhile, AIS, inspired by the human immune system, stands out for their adaptability and continuous learning, making them highly effective in recognizing complex cases. Artificial intelligence has enormous potential to improve the diagnosis and treatment of tuberculosis, making medical care more efficient and accessible. This study presents solutions that can enhance diagnostic accuracy and efficiency, enabling faster and more targeted interventions. By combining these technologies with traditional methods, efforts to combat tuberculosis can be optimized, reducing its spread and global mortality.
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