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This review explores 60 years of computer applications in clinical microbiology, focusing on artificial intelligence (AI) and machine learning (ML) for antibiotic selection to combat rising antimicrobial resistance.

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Area of Science:

  • Clinical Microbiology
  • Artificial Intelligence
  • Machine Learning

Background:

  • The increasing global threat of antimicrobial resistance necessitates advanced strategies for effective antibiotic use.
  • Early expert systems like Mycin (1976) showed promise but faced limitations in clinical integration.
  • Machine learning (ML) offers a solution to overcome expert system constraints by incorporating complex variables.

Purpose of the Study:

  • To review the evolution of computer applications in clinical microbiology over 60 years.
  • To highlight the potential of AI and ML in guiding antibiotic selection for bacterial infections.
  • To discuss the role of AI in discovering novel antimicrobial agents.

Main Methods:

  • Historical review of computer applications in clinical microbiology.
  • Discussion of AI/ML program design incorporating diverse clinical and epidemiological variables.
  • Exploration of AI's role in antimicrobial drug discovery.

Main Results:

  • Early AI systems like Mycin were limited by rule-based constraints.
  • Comprehensive AI/ML programs can integrate multiple factors influencing bacteria/drug interactions.
  • AI holds promise for identifying new antimicrobial therapies.

Conclusions:

  • AI and ML are crucial for optimizing antibiotic selection and combating antimicrobial resistance.
  • Future AI/ML applications in clinical microbiology can enhance treatment outcomes and drug discovery.