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How AI can help us beat AMR.

Autumn Arnold1,2,3, Stewart McLellan1,2,3, Jonathan M Stokes4,5,6

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Artificial intelligence (AI) offers solutions for antimicrobial resistance (AMR). This review covers AI applications in diagnosing bacterial infections, tracking AMR, and discovering new antibiotics, highlighting current methods and limitations.

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Area of Science:

  • Microbiology
  • Infectious Diseases
  • Computational Biology

Background:

  • Antimicrobial resistance (AMR) poses a significant global health challenge.
  • Rapid advancements in artificial intelligence (AI) and computational power are enabling new approaches in biological research.

Purpose of the Study:

  • To review the current applications of AI in addressing bacterial infections.
  • To explore AI's role in diagnostics, AMR surveillance, and antibiotic discovery.
  • To identify key considerations and limitations for AI implementation in these areas.

Main Methods:

  • Literature review of contemporary AI models.
  • Analysis of AI applications across diagnostics, surveillance, and drug discovery.
  • Discussion of practical considerations and existing challenges.

Main Results:

  • AI models are increasingly utilized for bacterial infection diagnostics.
  • AI facilitates enhanced surveillance of antimicrobial resistance patterns.
  • AI contributes to the discovery pipeline for novel antibiotics.

Conclusions:

  • AI presents a powerful toolkit for combating antimicrobial resistance.
  • Further research and development are needed to overcome current limitations in AI application.
  • Strategic implementation of AI can significantly impact public health outcomes related to infectious diseases.