ReviewFrontiers in cellular and infection microbiology2024
Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.
Review in Frontiers in cellular and infection microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 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
- Integrative Modular, Network-Based, and Machine Learning Framework for Predicting Accessory Genome Functions and Virulence in Escherichia coli O157:H7.MicrobiologyOpen · 2026Article
- An early prediction model of steroid dependence or frequent relapses in nephrotic children: a multicentre retrospective cohort study in China.Clinical kidney journal · 2026Article
- [Advances in the Application of Artificial Intelligence in Clinical Microbiological Testing].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Review
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
- Antimicrobial Resistance: The Answers.British journal of biomedical science · 2026Review
- Artificial intelligence applications in biofilm research: computational approaches for biofilm analysis and antimicrobial prediction.Frontiers in microbiology · 2026Review
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- The role of the gut microbiome in antibiotic-driven antimicrobial resistance.Frontiers in microbiology · 2026Review
- An AI-Driven Antimicrobial Resistance Surveillance Framework for Low-Resource Settings: A Pilot Study from Mogadishu, Somalia.Infection and drug resistance · 2026Article
- Carbapenem-resistant Gram-negative pathogens: molecular epidemiology, diagnostic advances, and emerging therapeutic strategies.Frontiers in microbiology · 2026Review
- Interpreting antimicrobial resistance from bacterial whole-genome sequencing: prediction tools, database fragmentation, analytical trade-offs, and harmonized reporting.Frontiers in microbiology · 2026Review
- Advances in the detection of Drug-Resistant bacteria: current trends and innovations.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Review
- Leveraging artificial intelligence for One Health: opportunities and challenges in tackling antimicrobial resistance - scoping review.One health outlook · 2025Article
- The Gut Microbiome and Colistin Resistance: A Hidden Driver of Antimicrobial Failure.International journal of molecular sciences · 2025Review
- Confronting the Antimicrobial Resistance Crisis in China: Emerging Superbugs, Genomic Surveillance, and Innovative Countermeasures.China CDC weekly · 2025Article
- From Farm to Fork: Antimicrobial-Resistant Bacterial Pathogens in Livestock Production and the Food Chain.Veterinary sciences · 2025Review
- Review
- Peptides in plant-microbe interactions: Functional diversity and pharmacological applications.Cell surface (Amsterdam, Netherlands) · 2025Review
- Proceedings of the Clinical Microbiology Open 2024: artificial intelligence applications in clinical microbiology.Journal of clinical microbiology · 2025Article
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 issue of antimicrobial resistance (AMR) in pathogenic microorganisms has emerged as a global public health crisis, posing a significant threat to the modern healthcare system. The advent of Artificial Intelligence (AI) and Machine Learning (ML) technologies has brought about revolutionary changes in this field. These advanced computational methods are capable of processing and analyzing large-scale biomedical data, thereby uncovering complex patterns and mechanisms behind the development of resistance. AI technologies are increasingly applied to predict the resistance of pathogens to various antibiotics based on gene content and genomic composition. This article reviews the latest advancements in AI and ML for predicting antimicrobial resistance in pathogenic microorganisms. We begin with an overview of the biological foundations of microbial resistance and its epidemiological research. Subsequently, we highlight the main AI and ML models used in resistance prediction, including but not limited to Support Vector Machines, Random Forests, and Deep Learning networks. Furthermore, we explore the major challenges in the field, such as data availability, model interpretability, and cross-species resistance prediction. Finally, we discuss new perspectives and solutions for research into microbial resistance through algorithm optimization, dataset expansion, and interdisciplinary collaboration. With the continuous advancement of AI technology, we will have the most powerful weapon in the fight against pathogenic microbial resistance in the future.
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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.