Artificial intelligence in drug resistance management

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.