A review study about Effects and Applications of Artificial Intelligence in Microbiology

The integration of artificial intelligence (AI) into microbiology heralds a transformative era for the field, offering unprecedented advancements in research and application. AI's prowess in data analysis, pattern recognition, and predictive modeling has been successfully harnessed to accelerate drug discovery, enhance microbial imaging and diagnostics, and predict antibiotic resistance. This review article delves into the multifaceted contributions of AI to microbiology, examining its impact on microbial genomics, metagenomics, and the monitoring of microbial ecosystems. We explore the synergy between AI and traditional microbiological methods, highlighting how machine learning and deep learning techniques are pushing the boundaries of microbial classification, ecology, pathogen epidemiology, and biotechnological innovation. As we stand on the cusp of the Fourth Industrial Revolution, AI's role in microbiology is not only augmenting human capabilities but also reshaping our understanding of microorganisms and their complex interactions with the environment and human health. This abstract encapsulates the essence of AI's role in microbiology, reflecting on both its current applications and future potential. AI has various applications in microbiology, including Big Data Analysis, Diagnosis and Interpretation, Prediction, and Prevention. In the field of clinical microbiology, AI plays a vital role in accelerating and enhancing the accuracy of clinical diagnoses. This includes Rapid Infection Diagnosis, Antibiotic Resistance, Drug Development, etc. Artificial Intelligence has the potential to revolutionize microbiology, especially clinical microbiology. However, to fully utilize this technology, there is a need for the development of data standards and specialized training for microbiology professionals. For a comprehensive exploration of the topic, you may refer to the detailed discussions and case studies presented in the full review.

کلیدواژه ها:

Artificial intelligence ، Microbiology ، Clinical Microbiology ، Machine learning ، Computational biology ، Computer vision