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

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.

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Publication Details

Journal
The Journal of Basic and Clinical Dentistry
Published
2024-11-06
DOI
https://doi.org/10.64471/30453003-24.1cll-02
Citations
1
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.23
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article

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

Raghavendra Vamsi Anegundi, Pradeep Kumar
1 citations
The Journal of Basic and Clinical Dentistry
Machine Learning in Bioinformatics
0.23
article

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

Raghavendra Vamsi Anegundi, Pradeep Kumar
article en
1 citations

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.

The Journal of Basic and Clinical DentistryVol. 1(1)
Saveetha University (IN)
Openalex Percentile: Top 39%
Machine Learning in Bioinformatics
0.23
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