ReviewCurrent genetics2021
The role of artificial intelligence in the battle against antimicrobial-resistant bacteria.
Review in Current genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Review
- Antibiotic Resistance Crisis: From Bacterial Bioprospecting to Artificial Intelligence.Environmental microbiology reports · 2025Review
- Leveraging artificial intelligence for One Health: opportunities and challenges in tackling antimicrobial resistance - scoping review.One health outlook · 2025Article
- Artificial intelligence-enhanced biosurveillance for antimicrobial resistance in sub-Saharan Africa.International health · 2025Review
- Advancing AMR Surveillance: Confluence of One Health and Big Data Integration : Converging One Health and Big Data for AMR.EcoHealth · 2025Review
- A Critical Review of the Prospect of Integrating Artificial Intelligence in Infectious Disease Diagnosis and Prognosis.Interdisciplinary perspectives on infectious diseases · 2025Review
- New antibiotics targeting Gram-negative bacilli.Le infezioni in medicina · 2025Review
- Lessons Learned from COVID-19 Pandemic in Combating Antimicrobial Resistance-Experience of Hong Kong, China.Microorganisms · 2024Article
- The challenge of antimicrobial resistance (AMR): current status and future prospects.Naunyn-Schmiedeberg's archives of pharmacology · 2024Review
- BacteSign: Building a Findable, Accessible, Interoperable, and Reusable (FAIR) Database for Universal Bacterial Identification.Biosensors · 2024Article
- Cryosphere: a frozen home of microbes and a potential source for drug discovery.Archives of microbiology · 2024Review
- Artificial intelligence in predicting pathogenic microorganisms' antimicrobial resistance: challenges, progress, and prospects.Frontiers in cellular and infection microbiology · 2024Review
- Innovative Phospholipid Carriers: A Viable Strategy to Counteract Antimicrobial Resistance.International journal of molecular sciences · 2023Review
- A Review of the Resistance Mechanisms forMedicina (Kaunas, Lithuania) · 2023Review
- Using random forest to predict antimicrobial minimum inhibitory concentrations of nontyphoidal Salmonella in Taiwan.Veterinary research · 2023Article
- Review
- Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates.Antibiotics (Basel, Switzerland) · 2022Review
- Antimicrobial Compounds from Microorganisms.Antibiotics (Basel, Switzerland) · 2022Review
- Article
- From Genome to Drugs: New Approaches in Antimicrobial Discovery.Frontiers in pharmacology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy development of new antimicrobials. In light of this, artificial intelligence in the form of machine learning has been viewed as a potential counter to delay the spread of AMR. With the aid of AI, there are possibilities to predict and identify AMR in bacteria efficiently. Furthermore, a combination of machine learning algorithms and lab testing can help to accelerate the process of discovering new antimicrobials. To date, many machine learning algorithms for antimicrobial-resistance discovery had been created and vigorously validated. Most of these algorithms produced accurate results and outperformed the traditional methods which relied on sequence comparison within a database. This mini-review will provide an updated overview of antimicrobial design workflow using the latest machine-learning antimicrobial discovery algorithms in the last 5 years. With this review, we hope to improve upon the current AMR identification and antimicrobial development techniques by introducing the use of AI into the mix, including how the algorithms could be made more effective.
Indexed as
Identifiers
33585980What 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.