Abstract The widespread contamination of the environment with antibiotic residues is a significant factor contributing to the global crisis of antimicrobial resistance. Antibiotics from various sources, such as effluents from municipal and hospital wastewater treatment plants, agricultural runoffs, discharges from pharmaceutical manufacturing and improper disposal of expired or unused medicines, create selective pressures in the spread of antibiotic resistance genes. These environmental reservoirs act as hotspots for horizontal gene transfer, facilitating the emergence of multidrug-resistant pathogens. Conventional detection methods including culture-based assays, chromatographic quantification, and molecular diagnostics, provide essential insights but are limited by low throughput, reduced sensitivity to new Antibiotic Resistance Genes, and challenges in real-time monitoring across complex environments. Recent advances, such as whole-genome sequencing, metagenomics, and biosensor-based detection, help to address these gaps by enabling more comprehensive surveillance of the resistome. Artificial intelligence further enhances these approaches by improving data interpretation and pattern recognition, thus complementing traditional and molecular methods rather than replacing them. This review examines the pathways of environmental antibiotic contamination, ecological and health impacts of Antimicrobial Resistance (AMR), and limitations of conventional detection methods. It aims to clarify how these pathways contribute to the AMR crisis, assess the effectiveness of existing surveillance techniques, and identify gaps in current research.
Singh et al. (Fri,) studied this question.