Volume 3 Number 1 2025
Integrating AI and ML Techniques in Modern Microbiology
Rabi Sankar Mondal1*, Lamia Akter2, Md Nazmul Alam Bhuiyan1
Applied IT & Engineering 3 (1) 1-8 https://doi.org/10.25163/engineering.3110323
Submitted: 29 April 2025 Revised: 20 July 2025 Published: 22 July 2025
Abstract
The integration of artificial intelligence (AI) and machine learning (ML) into the field of microbiology is making a significant impact on both research and clinical practices. These technologies are helping us better understand microbial systems, improve diagnostic accuracy, speed up drug discovery, and tailor treatments to individual patients. Some exciting applications of AI in microbiology include helping with genome sequencing, predicting antimicrobial resistance, developing vaccines, and analyzing microbiomes. In industrial settings, AI is transforming quality control and safety in the pharmaceutical, food, and cosmetic industries. However, despite these advancements, there are still challenges that prevent us from fully realizing the potential of AI and ML in microbiology. Issues such as inconsistent data quality, the complexity of algorithms that can be difficult to interpret, a lack of regulatory guidelines, and ethical concerns surrounding data privacy and algorithm bias must be addressed. There is a pressing need for collaboration across various fields, transparent development of models, and cooperation among academic institutions and healthcare providers globally. Looking to the future, we can expect innovations in deep learning, hybrid modeling techniques, and the development of international standards and policies to drive further advancements. With responsible implementation and ongoing research, AI and ML are set to become essential tools in microbiology, offering innovative solutions to persistent challenges in healthcare, diagnostics, and environmental monitoring. This review focuses on the current uses, challenges, and future directions of AI in microbiology, highlighting the importance of ethical considerations, collaboration, and data-driven strategies to achieve meaningful and sustainable outcomes.
Keywords: Artificial intelligence, machine learning, microbiology, antimicrobial resistance, personalized medicine
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