ReviewJournal of medical systems2024
From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.
Review in Journal of medical systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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
33 citing papers in PubMed.
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Computational Genomics for Resistome Characterization: Current Advancements and Future Challenges Under a One Health Perspective.Antibiotics (Basel, Switzerland) · 2026Review
- Article
- Artificial Intelligence in Bacteriophage Science: A Comprehensive Narrative Review of Applications, Challenges, and Translational Opportunities.Antibiotics (Basel, Switzerland) · 2026Review
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- Machine learning-driven decision support for antibiotic optimization in typhoid fever based on patient profiles.BMC medical informatics and decision making · 2026Article
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Innovative Approaches to Combat Antimicrobial Resistance: A Review of Emerging Therapies and Technologies.Probiotics and antimicrobial proteins · 2026Review
- CAMPER: mechanistic artificial intelligence for designing peptides that target MRSA persisters.Nature communications · 2026Article
- Artificial intelligence drives the identification and screening of novel antibiotics and antimicrobial peptides.Briefings in bioinformatics · 2026Review
- Design, synthesis, molecular docking, and antimicrobial evaluation of hybrid peptides incorporating unnatural amino acids with enhanced hydrophobic sidechains.RSC advances · 2026Article
- The Future of Antibiotics and Artificial Intelligence: Some Thoughts from Discovery to Bedside.Infectious diseases and therapy · 2026Review
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- Artificial intelligence in optimizing antimicrobial therapy for gastro-renal disorders.Frontiers in cellular and infection microbiology · 2026Review
- Antibiotic resistance inFrontiers in microbiology · 2026Review
- The impact of artificial intelligence on the prescribing, selection, resistance, and stewardship of antimicrobials: a scoping review.BMC infectious diseases · 2025Article
- Smart hybrid nanomaterials for chronic infections: microbiome-responsive and sustainable therapeutic platforms.Journal of nanobiotechnology · 2025Review
- From sequence to signature: Machine learning uncovers multiscale feature landscapes that predict AMR across ESKAPE pathogens.bioRxiv : the preprint server for biology · 2025Article
- Leveraging artificial intelligence for One Health: opportunities and challenges in tackling antimicrobial resistance - scoping review.One health outlook · 2025Article
- Beyond Antibiotics: Repurposing Non-Antibiotic Drugs as Novel Antibacterial Agents to Combat Resistance.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.
Indexed as
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.