Artificial Intelligence in Bacterial Infectious Diseases: From Diagnosis to Drug Discovery

Abstract

Bacterial infectious diseases remain a formidable global health challenge, exacerbated by the relentless rise of antimicrobial resistance (AMR) and the emergence of novel pathogens. Artificial intelligence (AI), encompassing machine learning (ML), deep learning (DL), and natural language processing (NLP), has emerged as a transformative paradigm across the entire spectrum of bacterial infection management. This review synthesizes recent advances in AI applications for bacterial infectious diseases, spanning rapid pathogen identification, antimicrobial susceptibility testing, genomic surveillance, epidemiological monitoring, antibiotic discovery, and clinical decision support. We highlight how AI-driven technologies are accelerating diagnostic timelines from days to minutes, enabling real-time resistance profiling, and uncovering novel therapeutic candidates. Furthermore, we examine the critical challenges impeding clinical translation, distinguishing between engineering-level obstacles, algorithmic-level tensions, and institutional-level barriers. By fostering interdisciplinary collaboration among clinicians, microbiologists, computational scientists, and policymakers, AI holds the potential to revolutionize our approach to bacterial infections and mitigate the looming AMR crisis.