Reviewnpj antimicrobials and resistance2025
How AI can help us beat AMR.
Review in npj antimicrobials and resistance, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for infection surveillance, risk stratification, and antimicrobial decision support in acute-care hospitals: a scoping review.Frontiers in artificial intelligence · 2026Pooled it
- Beyond the Barrier: Overcoming Ocular Antimicrobial Resistance Through AI and Novel Therapeutics.International journal of molecular sciences · 2026Review
- Artificial Intelligence in Food-Nutrition-Health Research: From Multimodal Data Integration to Precision Intervention.Journal of food science · 2026Review
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- Current State of the Fight Against Antimicrobial Resistance: What Are the Different Strategies for Tomorrow?Antibiotics (Basel, Switzerland) · 2026Review
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- ASP-COMPLEX: redefining antimicrobial stewardship to improve outcomes in high-risk patients with severe infections.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2026Review
- Next-Generation Target Discovery in ESKAPE Pathogens: An AI-Driven Framework from Omics-Based to Systems-Level Modeling and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Special Issue "Novel Mechanisms of Bacterial Antibiotic Resistance and Strategies to Fight Them".International journal of molecular sciences · 2026Article
- Hidden failure modes of large language models in healthcare-associated infection surveillance: a structured evaluation using NHSN definitions.Infection control and hospital epidemiology · 2026Article
- Artificial intelligence for early detection and risk prediction of antimicrobial resistance in aquatic ecosystems.npj antimicrobials and resistance · 2026Review
- Emerging synthetic biology-assisted technologies for overcoming antibiotic resistance: CRISPR-Cas, bacteriophage, microbiome, and metabolic engineering-based solutions.Journal of microbiology (Seoul, Korea) · 2026Review
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- Search for Specific Inhibitors Targeting Type IA Topoisomerases.Journal of molecular biology · 2026Review
- Containment of antimicrobial resistance for strengthening global public health security: Biorisk management perspectives.Biosafety and health · 2026Review
- Genome-wide screening of antibiotic survival genes in Escherichia coli MG1655.BMC microbiology · 2026Article
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- The role of the gut microbiome in antibiotic-driven antimicrobial resistance.Frontiers in microbiology · 2026Review
- stackPredAMR-a stacked random forest approach improves AMR phenotype prediction for multiple species and antimicrobial agents.Bioinformatics advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) is an urgent public health threat. Advancements in artificial intelligence (AI) and increases in computational power have resulted in the adoption of AI for biological tasks. This review explores the application of AI in bacterial infection diagnostics, AMR surveillance, and antibiotic discovery. We summarize contemporary AI models applied to each of these domains, important considerations when applying AI across diverse tasks, and current limitations in the field.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.