ReviewAntibiotics (Basel, Switzerland)2025
The Role of Artificial Intelligence and Machine Learning Models in Antimicrobial Stewardship in Public Health: A Narrative Review.
Review in Antibiotics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses 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
38 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Pooled it
- Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.Cancer medicine · 2025Pooled it
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- From AI anxiety to educational opportunity: equitable and practical uses of artificial intelligence in STEM education.Journal of microbiology & biology education · 2026Article
- Real-world evidence on antibiotic use among privately insured beneficiaries in Saudi Arabia: a retrospective analysis of claims data.BMJ open · 2026Article
- Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.The Journal of antibiotics · 2026Review
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
- Addressing antimicrobial resistance: Current challenges, emerging strategies, and an AI-powered, community-driven approach.PNAS nexus · 2026Review
- Quorum-sensing, microbiome interactions, and emerging artificial intelligence-assisted anti-virulence strategies in Salmonella Typhi: a critical review of translational opportunities and challenges.Archives of microbiology · 2026Review
- A bibliometric analysis of the application trends of information technology in antimicrobial stewardship within hospitals.European journal of hospital pharmacy : science and practice · 2026Article
- Artificial intelligence in combating challenges in antimicrobial resistance: a narrative review.Infection prevention in practice · 2026Review
- Machine learning-driven decision support for antibiotic optimization in typhoid fever based on patient profiles.BMC medical informatics and decision making · 2026Article
- Antimicrobial Consumption and Resistance Dynamics Across Healthcare Level: Global Evidence and Stewardship Implications.Pathogens (Basel, Switzerland) · 2026Review
- Artificial intelligence for early detection and risk prediction of antimicrobial resistance in aquatic ecosystems.npj antimicrobials and resistance · 2026Review
- Antimicrobial stewardship from a One Health perspective.Nature reviews. Microbiology · 2026Review
- The Promise, Pitfalls, and Practicalities of Precision Education in Obstetrics and Gynecology.O&G open · 2026Article
- Role of Artificial Intelligence in Infectious Diseases and Antimicrobial Resistance: A Comprehensive Review on Diagnostic, Treatment, and Prevention Aspects.Saudi medical journal · 2026Review
- Containment of antimicrobial resistance for strengthening global public health security: Biorisk management perspectives.Biosafety and health · 2026Review
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- Development of a capability framework for antimicrobial stewardship specialist health professionals working in the NHS in England: utilizing Delphi methodology.The Journal of antimicrobial chemotherapy · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) poses a critical global health threat, necessitating innovative approaches in antimicrobial stewardship (AMS). Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools in this domain, enabling data-driven interventions to optimize antibiotic use and combat resistance. This comprehensive review explores the multifaceted role of AI and ML models in enhancing antimicrobial stewardship efforts across healthcare systems. AI-powered predictive analytics can identify patterns of resistance, forecast outbreaks, and guide personalized antibiotic therapies by leveraging large-scale clinical and epidemiological data. ML algorithms facilitate rapid pathogen identification, resistance profiling, and real-time monitoring, enabling precise decision making. These technologies also support the development of advanced diagnostic tools, reducing the reliance on broad-spectrum antibiotics and fostering timely, targeted treatments. In public health, AI-driven surveillance systems improve the detection of AMR trends and enhance global monitoring capabilities. By integrating diverse data sources-such as electronic health records, laboratory results, and environmental data-ML models provide actionable insights to policymakers, healthcare providers, and public health officials. Additionally, AI applications in antimicrobial stewardship programs (ASPs) promote adherence to prescribing guidelines, evaluate intervention outcomes, and optimize resource allocation. Despite these advancements, challenges such as data quality, algorithm transparency, and ethical considerations must be addressed to maximize the potential of AI and ML in this field. Future research should focus on developing interpretable models and fostering interdisciplinary collaborations to ensure the equitable and sustainable integration of AI into antimicrobial stewardship initiatives.
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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.