Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities

Abstract

Abstract Antimicrobial resistance (AMR) poses a significant global public health threat, and efforts to mitigate it have been aided by artificial intelligence (AI) methods. Key areas of applications include rapid diagnostics, drug discovery and repurposing, surveillance and predictive modelling, and antibiotic stewardship. This review aims to summarize key literature on AI applications for mitigating AMR including original research articles from PubMed and Scopus published from October 2024 to 2025 to summarize and find out the way ahead for successful application of AI. The key search terms included were AMR, AI and applications such as diagnostics, drug discovery and repurposing, surveillance, predictive modelling and stewardship. The data used for these applications, the techniques applied and the predictive targets have undergone significant expansion, along with the focus on model deployment, external validation and model interpretability. Although more complex models, such as neural networks and transformers, have been experimented with, classical machine learning (ML) models still dominate the AMR prediction space, while large language models are also being tested for prediction, as well as antibiotic stewardship. For drug discovery, data mining for antimicrobial peptides from different sources is a major application. Predictive modelling using next-generation sequencing data has been the most studied. The application of AI/ML to large and complex data from multiple sources could provide a promising arena for developing clinically translational tools. With more data availability, regulatory measures, real-world validation and transparency, there is scope for responsibly integrating innovative technology into clinical practice.