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The integration of artificial intelligence (AI) into microbiology has the transformative potential to advance our understanding and treatment of microbial systems. This review examines various applications of AI in microbiology, including activities such as predicting drug targets and vaccine candidates, identifying microorganisms responsible for infectious diseases, classifying drug resistance to antimicrobial drugs, predicting disease outbreaks, as well as investigating interactions between microorganisms, quality assurance, Identification of bacteria and compliance with health standards. We summarized key AI algorithms such as Naive Bayes, Support Vector Machines, Deep Learning, and Random Forests used in various microbiological studies. We also address challenges and criticisms associated with AI in microbiology. Finally, we discuss the prospects of AI, including advances in personalized medicine, reducing antimicrobial resistance, microbiome research, rapid diagnostics, environmental microbiology, and synthetic biology. Our review includes a comprehensive analysis of recent literature, identifying and evaluating AI algorithms used in microbiological research. We used systematic searches and inclusion criteria to ensure the relevance and quality of the reviewed studies. Despite the significant advances that AI brings to microbiology, challenges such as data heterogeneity, model transparency, and ethical considerations must be addressed. Interdisciplinary collaboration and rigorous validation of AI models are crucial to overcome these challenges. The future of AI in microbiology looks promising with potential applications in personalized medicine, rapid pathogen detection, and environmental monitoring. AI provides a powerful tool for microbiological research, with the potential to revolutionize our diagnosis, treatment and understanding of microbial ecosystems.
Mohseni et al. (Sat,) studied this question.