Key points are not available for this paper at this time.
In this mini-review, we delve into the transformative impact of artificial intelligence (AI) and machine learning (ML) in the field of microbiology. The paper provides a brief overview of various domains where AI is reshaping practices, including clinical diagnostics, drug and vaccine discovery, and public health management. Our discussion spotlights the implementation of convolutional neural networks for enhanced pathogen identification, the advancements in point-of-care diagnostics, and the emergence of new antimicrobials to tackle resistant strains. The application of AI in epidemiology, microbial ecology and forensic microbiology is also outlined, underscoring its proficiency in deciphering complex microbial interactions and forecasting disease outbreaks. We critically examine the challenges in AI application, such as ensuring data quality and overcoming algorithmic constraints, and stress the necessity for interpretable AI models that align with medical and ethical standards. We address the intricacies of digitalization in microbiology diagnostics, emphasizing the need for efficient data management in laboratory and clinical environments. Looking forward, we identify key directions for AI in microbiology, particularly focusing on developing adaptable, self-updating AI models and their integration into clinical settings. We conclude by highlighting AI's potential to revolutionize microbiological diagnostics and infection control, significantly influencing patient care and public health. This review serves as an invitation to explore AI's integration into microbiology, showcasing its role in evolving current methodologies and propelling future innovations.
Tsitou et al. (Sun,) studied this question.