From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance – a Comprehensive Review

The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.

Bibliographic Details

Main Authors:	Pérez de Lastra, José Manuel, Wardell, Samuel J. T., Pal, Tarun, de la Fuente-Núñez, César, Pletzer, Daniel
Other Authors:	Ministerio de Ciencia e Innovación (España)
Format:	artículo biblioteca
Language:	English
Published:	Springer Nature 2024-08-01
Subjects:	AI/ML, pathogen identification, antibiotic resistance, antibiotic stewardship, personalized treatment, antimicrobial peptides, artificial intelligence, machine learning, antimicrobial resistance,
Online Access:	http://hdl.handle.net/10261/364991
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