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Updated: May 20, 2025

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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Artificial intelligence in drug resistance management.

Amir Elalouf1, Hadas Elalouf1, Ariel Rosenfeld2

  • 1Department of Management, Bar-Ilan University, 5290002 Ramat Gan, Israel.

3 Biotech
|April 16, 2025
PubMed
Summary

Artificial intelligence (AI), including machine learning (ML), is revolutionizing antimicrobial resistance (AMR) management by predicting drug resistance and identifying new antibiotics. AI offers promising interventions for global public health, despite challenges in data and ethics.

Keywords:
AI applicationsAntimicrobial resistanceArtificial intelligenceDrug resistance management

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Area of Science:

  • Microbiology
  • Computational Biology
  • Public Health

Background:

  • Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative management strategies.
  • Traditional methods for combating AMR are increasingly insufficient, driving the need for advanced computational approaches.

Purpose of the Study:

  • To review the application of artificial intelligence (AI), specifically deep learning and machine learning (ML), in addressing antimicrobial resistance (AMR).
  • To highlight AI's role in predicting resistance patterns, identifying novel antibiotics, and optimizing antibiotic usage.

Main Methods:

  • Review of studies applying AI/ML algorithms (Naïve Bayes, Decision Trees, Random Forest, SVM, ANN) to AMR data.
  • Analysis of AI's impact on predicting resistance phenotypes, identifying drug candidates, and detecting AMR-associated mutations.

Main Results:

  • AI models significantly improve the prediction of antimicrobial drug resistance patterns.
  • Machine learning algorithms have successfully identified novel antibiotic candidates and optimized antibiotic use.
  • AI facilitates the detection of AMR-associated mutations, providing insights into resistance mechanisms and spread.

Conclusions:

  • AI is a powerful tool for combating AMR, with potential to improve patient outcomes and disease management.
  • Overcoming challenges such as data scarcity, privacy, ethical considerations, and fostering collaboration is crucial for realizing AI's full potential in AMR.
  • AI applications in AMR management have significant implications for global public health strategies.