Artificial intelligence-driven advances in medical microbiology: a review on next-generation diagnosis, surveillance, and therapeutics

The field of health science, medical microbiology is key aspect for diagnosis, pathogen surveillance, and therapeutic interventions. Integration of artificial intelligence undergoing a profound transformation in the advances of medical microbiology. Artificial intelligence has emerged as a transformative force across biomedical sciences. In perspectives of medical microbiology, artificial intelligence algorithms are being rapidly adopted to address critical challenges, from pathogen identification to outbreak prediction and antimicrobial resistance (AMR) management. The present study insights latest landscape of artificial intelligence-driven approaches in revealing new breakthroughs, advancement in technological, improved clinical applications, and future directions. This review focusses on next generation approaches such as convolutional neural networks (CNNs) that have enabled rapid pathogen identification through Gram stain and MALDI-TOF-MS analysis. Random Forests and Support Vector Machines (SVMs) predict AMR, supporting antibiotic susceptibility testing and Recurrent Neural Networks (RNNs), facilitate early outbreak prediction using epidemiological data. The environmental surveillance is enhanced by artificial intelligence-driven analysis of biosensor data, and lab automation benefits from expert systems and reinforcement learning, improving efficiency and reducing error. The present advance study will be helpful to set more transformative role of artificial intelligence across the microbiology diagnostic spectrum.