Genomic Surveillance and Prediction of Antibiotic Resistance Trends Using AI Tools

Abstract

Antibiotic resistance (AMR) is a global public health crisis that threatens the efficacy of many life-saving antibiotics. As resistance patterns continue to evolve and spread, traditional surveillance methods struggle to keep pace with the complexity and scale of the issue. Genomic surveillance, particularly when integrated with advanced Artificial Intelligence (AI) tools, offers a powerful solution to track, predict, and mitigate the emergence of resistant bacterial strains. This paper explores the role of AI in genomic surveillance, highlighting how machine learning and deep learning algorithms can analyze vast genomic datasets to identify resistance genes, mutations, and emerging resistance trends. By combining genomic, clinical, and environmental data, AI-powered models can predict resistance patterns, enabling early detection and more targeted public health interventions. The paper examines various AI techniques, including supervised and unsupervised learning, and their application in forecasting antibiotic resistance in pathogens like Escherichia coli and Mycobacterium tuberculosis. Despite the promising potential, challenges such as data quality, model generalizability, and ethical concerns persist. Nevertheless, the integration of AI into genomic surveillance holds transformative potential for enhancing global efforts to combat AMR, providing real-time insights and more effective strategies for antibiotic stewardship. The paper concludes by discussing future directions for AI in genomic surveillance, emphasizing the need for further research, collaboration, and global data sharing to improve predictive capabilities and combat antibiotic resistance effectively.