Articles

The rise of antimicrobial resistance (AMR) presents a significant health threat, resulting in around 1.27 million fatalities each year. The COVID-19 pandemic underscored the critical demand for groundbreaking antiviral therapies and reliable diagnostic methods. AI is crucial in forecasting AMR by examining bacterial whole genome sequences, enhancing treatment approaches and monitoring. Conventional approaches frequently miss new AMR factors, resulting in errors in detecting resistant strains. Machine learning (ML) has demonstrated its importance in predicting resistance trends, with instances of effective models reaching as high as 95% accuracy in estimating minimum inhibitory concentrations (MICs). AI supports applications in optimizing antibiotic utilization, forecasting infectious diseases, and improving drug development. Automated systems greatly enhance AMR genomic monitoring by providing actionable information for infection management. Although there have been notable advancements, obstacles persist in AI adoption, especially the absence of extensive clinical trials, which could hinder confidence in AI relative to traditional approaches. Tackling AMR necessitates collaborative actions within the One Health approach, combining strategies for human, animal, and environmental health. In conclusion, although AI and big data offer significant promise for addressing AMR, continuous research and improvement are necessary to address challenges and improve clinical applications.