Artificial intelligence in the diagnosis and identification of bacterial pathogens: Current advances and future...

Bacterial infections remain a major global health challenge, which are responsible for significant morbidity, mortality, and increasing antimicrobial resistance worldwide. Rapid and accurate identification of bacterial pathogens is therefore essential for guiding appropriate antimicrobial therapy, improving patient outcomes, and supporting effective infection control strategies. However, conventional microbiological diagnostic methods such as culture, microscopy, and biochemical testing are often time-consuming, labor-intensive, and dependent on expert interpretation. In recent years, artificial intelligence (AI) has increasingly emerged as a powerful tool capable of transforming clinical microbiology by enabling rapid analysis of complex biomedical data. This review aims to provide a comprehensive overview of the role of AI in the diagnosis and identification of bacterial pathogens and to discuss its current applications, advantages, limitations, and future perspectives. Key AI-assisted technologies, including machine learning and deep learning algorithms such as Random Forest, Support Vector Machines, Artificial Neural Networks, and Convolutional Neural Networks, are increasingly applied to diagnostic platforms, including automated microscopy, next-generation genomic sequencing, MALDI-TOF mass spectrometry, and clinical data analysis systems. Current evidence indicates that AI-assisted diagnostic approaches can substantially improve the speed, accuracy, and automation of microbial pathogen detection while enabling large-scale analysis of microbiological and clinical datasets. In the future, AI-integrated microbiology laboratories, rapid diagnostic platforms, genomic surveillance systems, and AI-assisted clinical decision support tools are expected to further enhance infectious disease diagnostics and contribute to improved antimicrobial stewardship and global infection management.