ArticleAntibiotics (Basel, Switzerland)2024
Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare's Future.
Article in Antibiotics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed.
- Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.The Journal of antibiotics · 2026Review
- Review
- Integrating machine learning and artificial intelligence in the management of Acinetobacter infections: a narrative review.Infection · 2026Review
- Special Issue "Novel Mechanisms of Bacterial Antibiotic Resistance and Strategies to Fight Them".International journal of molecular sciences · 2026Article
- Microbial medicines: Unlocking the therapeutic potential of the microbiome in cancer treatment.Journal of controlled release : official journal of the Controlled Release Society · 2026Review
- Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Pharmacokinetics and Customized Dosing of Vancomycin in Adult Patients With Hematological Malignancies: Status, Challenges, and Opportunities.Pharmacology research & perspectives · 2026Review
- Genome-wide screening of antibiotic survival genes in Escherichia coli MG1655.BMC microbiology · 2026Article
- Observational
- Harnessing biogenic nanoparticles for combating antibiotic resistance: green synthesis, mechanistic insights, and biotechnological applications.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial intelligence in optimizing antimicrobial therapy for gastro-renal disorders.Frontiers in cellular and infection microbiology · 2026Review
- The impact of artificial intelligence on the prescribing, selection, resistance, and stewardship of antimicrobials: a scoping review.BMC infectious diseases · 2025Article
- Neonatal and pediatric sepsis: Microbiological insights, diagnostic innovations, and antimicrobial challenges.World journal of clinical pediatrics · 2025Review
- Enhancing Global Health Security in Sub-Saharan Africa: The case for integrated One Health surveillance against zoonotic diseases and environmental threats.One health (Amsterdam, Netherlands) · 2025Review
- Guiding Antibiotic Therapy with Machine Learning: Real-World Applications of a CDSS in Bacteremia Management.Life (Basel, Switzerland) · 2025Article
- Managing Nonunions and Fracture-Related Infections-A Quarter Century of Knowledge, and Still Curious: A Narrative Review.Journal of clinical medicine · 2025Review
- Review
- Leveraging artificial intelligence for One Health: opportunities and challenges in tackling antimicrobial resistance - scoping review.One health outlook · 2025Article
- Advancing AMR Surveillance: Confluence of One Health and Big Data Integration : Converging One Health and Big Data for AMR.EcoHealth · 2025Review
- The Changing Landscape of Antibiotic Treatment: Reevaluating Treatment Length in the Age of New Agents.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibiotic resistance poses a significant threat to global public health due to complex interactions between bacterial genetic factors and external influences such as antibiotic misuse. Artificial intelligence (AI) offers innovative strategies to address this crisis. For example, AI can analyze genomic data to detect resistance markers early on, enabling early interventions. In addition, AI-powered decision support systems can optimize antibiotic use by recommending the most effective treatments based on patient data and local resistance patterns. AI can accelerate drug discovery by predicting the efficacy of new compounds and identifying potential antibacterial agents. Although progress has been made, challenges persist, including data quality, model interpretability, and real-world implementation. A multidisciplinary approach that integrates AI with other emerging technologies, such as synthetic biology and nanomedicine, could pave the way for effective prevention and mitigation of antimicrobial resistance, preserving the efficacy of antibiotics for future generations.
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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.