Integrating molecular diagnostics and artificial intelligence in chronic microbial disease

Chronic microbial diseases, often driven by biofilm formation, pose a persistent global health burden due to their complex diagnosis, resistance mechanisms, and prolonged disease courses. Conventional diagnostic methods are time-consuming and often insufficient for early, precise detection and prognosis. Recent advances in molecular diagnostics, including PCR, hybridization, next-generation sequencing, and CRISPR-based assays, have enabled rapid, non-invasive, and highly sensitive detection of pathogens and resistance markers. Complementary "omics" technologies, like genomics, proteomics, and metabolomics, provide deeper insights into disease pathways, aiding in personalized treatment strategies. Furthermore, the integration of artificial intelligence (AI) and big data analytics enhances the interpretation of complex molecular datasets, enabling pattern recognition, risk prediction, and tailored therapeutic decisions. This manuscript reviews current tools and emerging technologies for the diagnosis and prognosis of chronic microbial diseases, highlighting the transformative potential of AI-driven precision medicine to improve patient outcomes through early detection, individualized treatment, and better disease management.