ArticleInfection and drug resistance2022
A New Hope in the Fight Against Antimicrobial Resistance with Artificial Intelligence.
Article in Infection and drug resistance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Cognitive sovereignty and decolonial public health: reclaiming epistemic authority in the global AI era.Frontiers in public health · 2026Article
- Artificial intelligence in drug resistance management.3 Biotech · 2025Review
- Healthcare Professionals' Knowledge and Attitudes Toward Antimicrobial Stewardship Programs in Aseer, Saudi Arabia: A Cross-Sectional Study.Risk management and healthcare policy · 2025Article
- The challenge of antimicrobial resistance (AMR): current status and future prospects.Naunyn-Schmiedeberg's archives of pharmacology · 2024Review
- Polymicrobial infection in cystic fibrosis and future perspectives for improving Mycobacterium abscessus drug discovery.npj antimicrobials and resistance · 2024Review
- Personal Care Products as a Contributing Factor to Antimicrobial Resistance: Current State and Novel Approach to Investigation.Antibiotics (Basel, Switzerland) · 2023Review
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
Recent years have witnessed the rise of artificial intelligence (AI) in antimicrobial resistance (AMR) management, implying a positive signal in the fight against antibiotic-resistant microbes. The impact of AI starts with data collection and preparation for deploying AI-driven systems, which can lay the foundation for some effective infection control strategies. Primary applications of AI include identifying potential antimicrobial molecules, rapidly testing antimicrobial susceptibility, and optimizing antibiotic combinations. Aside from their outstanding effectiveness, these applications also express high potential in narrowing the burden gap of AMR among different settings around the world. Despite these benefits, the interpretability of AI-based systems or models remains vague. Attempts to address this issue had led to two novel explanation techniques, but none have shown enough robustness or comprehensiveness to be widely applied in AI and AMR control. A multidisciplinary collaboration between the medical field and advanced technology is therefore needed to partially manage this situation and improve the AI systems' performance and their effectiveness against drug-resistant pathogens, in addition to multiple equity actions for mitigating the failure risks of AI due to a global-scale equity gap.
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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.