The benefits and biases of artificial intelligence in microbiology.

Abstract

Microbiology has been greatly advanced by the incorporation of Artificial Intelligence (AI) by improving the classification, isolation and detection of microorganisms in several fields where microbial exploration is carried out. With the ability to process large volumes of data from genomic sequences, biochemical profiles and imaging, AI enables faster and more accurate microbial identification than conventional techniques. In microbiome study, AI decodes complex microbial communities in environments, the human body and air by identifying unknown species and their ecological roles. AI is a valuable tool in predicting antimicrobial resistance (AMR) by detecting resistance genes within microbial genomes and aiding in effective treatment strategies. AI-powered diagnostic tools and biosensors offer rapid pathogen detection, while Natural Language Processing (NLP) aids in tracking emerging microbial threats through scientific literature and health databases. In drug and vaccine development, AI accelerates the discovery process by simulating molecular interactions and predicting outcomes, saving time and resources. Most AI models, especially deep learning tools, however, lack transparency leading to difficult result interpretation. Additionally, over-fitting and limited generalizability of AI models are also concerns, along with ethical and legal issues like data privacy in microbiome research and clinical settings. Prior review articles have examined AI's impact in singular microbiological settings, and special attention has been paid to AI applications in medical microbiology. This review article reaches out and analyzes AI applications across general, industrial, medical, and environmental microbiology. It also outlines how mitigation strategies like inclusive dataset design, algorithm vigilance, evaluation techniques like Translational Evaluation of Healthcare AI (TEHAI) and emerging directions of analyses like federated learning and Explainable Artificial Intelligence (XAI) address the risks associated with AI use like bias and reduced interpretability.