Revolutionizing Medical Microbiology: The Role of Artificial Intelligence in Microbial Diagnostics and Research

Conventional microbial diagnostics face challenges related to specimen handling, microbial isolation, result interpretation, and delays in antimicrobial resistance determination. The integration of artificial intelligence (AI), particularly machine learning (ML), offers efficient and innovative solutions to these limitations. This review emphasizes the importance of standardized sample processing and highlights recent advances in AI-based approaches for microbial classification, microorganism interaction analysis, and enhanced microscopy. Applications of convolutional neural networks in automated bacterial identification, digital pathology, colony counting, antimicrobial susceptibility testing, and disease surveillance are discussed, with examples including malaria, mycobacteria, and SARS-CoV-2. Despite ethical concerns regarding data privacy, integrity, and bias, AI-driven diagnostics demonstrate robust potential to transform diagnostic microbiology.