Can Large Language Models Predicting Antimicrobial Resistance Genes Prevent the Spread of the Oral Resistome?

Abstract

Antimicrobial resistance (AMR) is an escalating concern worldwide. This article comments on the role of large language models (LLMs) in predicting AMR-related genes within the oral microbiome, which could be pivotal in controlling the dissemination of the oral resistome. By identifying patterns and forecasting the presence of AMR genes, LLMs may support public health initiatives and enhance personalized healthcare strategies. Despite their promise, challenges such as data reliability and ethical considerations remain. Therefore, the integration of LLMs with conventional approaches and health policies is essential for successfully addressing AMR.