Loading
Life sciences · Journal article
Journal of Public Health and Preventive Medicine · September 23, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
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.