Integrating Sequencing Methods with Machine Learning for Antimicrobial Susceptibility Testing in Pediatric Infections: Current Advances and Future Insights

Antimicrobial resistance (AMR) is a significant challenge in pediatric patients, especially those with life-threatening conditions. Advances in sequencing methods have improved detection of pathogens and resistance genes. However, discrepancies between resistance gene detection and antimicrobial susceptibility testing can hinder clinical application. Machine learning (ML) can help by integrating large-scale resistance data with sequencing outcomes, enabling more accurate predictions of drug susceptibility. This review aims to promote ML-based predictions in clinical practice.