Artificial Intelligence for Antimicrobial Resistance Prediction: Challenges and Opportunities towards Practical Implementation

Abstract

Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It is very important to understand and apply effective strategies to counter the impact of AMR and its mutation from a medical treatment point of view. The intersection of artificial intelligence (AI), especially deep learning/machine learning, has led to a new direction in antimicrobial identification. Furthermore, presently, the availability of huge amounts of data from multiple sources has made it more effective to use these artificial intelligence techniques to identify interesting insights into AMR genes such as new genes, mutations, drug identification, conditions favorable to spread, and so on. Therefore, this paper presents a review of state-of-the-art challenges and opportunities. These include interesting input features posing challenges in use, state-of-the-art deep-learning/machine-learning models for robustness and high accuracy, challenges, and prospects to apply these techniques for practical purposes. The paper concludes with the encouragement to apply AI to the AMR sector with the intention of practical diagnosis and treatment, since presently most studies are at early stages with minimal application in the practice of diagnosis and treatment of disease.

Keywords: antimicrobial resistance genes; artificial intelligence; challenges and opportunities; deep learning; machine learning.

Figure 1

Overall process of applying machine-learning/deep-learning models in AMR identification.

Figure 2

Confusion matrix for binary output classification problem.

Figure 3

AI can be applied on antimicrobials to obtain different objectives such as clinical care, drug development, surveillance, identification of new AMR etc.

Figure 4

Combination of moral and AI-based frameworks for CDSS.

Figure 5

Efficiency of AST methods based on AI and conventional techniques.