Predicting antimicrobial resistance for precision medicine

Antibiotics are among medicine's greatest successes, but resistance evolution threatens their continued efficacy. Decades of research have deepened our understanding of the mechanisms and evolutionary dynamics of antimicrobial resistance. More recently, advances in machine learning (ML) and artificial intelligence (AI) show promise in predicting antimicrobial resistance in pathogens based on rapid whole-genome sequencing and other accessible data. In this perspective, we highlight advances in understanding the mechanisms and spread of antimicrobial resistance. We discuss how this knowledge, coupled with ML- and AI-based approaches, can inform the prediction of resistance and a precision-medicine strategy that targets pathogenic bacteria specifically, thereby limiting resistance evolution and collateral damage to the microbiome. These accurate predictions of bacterial vulnerabilities will enable the adaptation of classical antimicrobial treatments with adjuvants, as well as the use of novel, narrow-spectrum therapeutics. Implementing these strategies, while also identifying key challenges, will help bring this strategy into clinical practice.