ReviewJournal of tropical medicine2025
A Focused Review on Commercially Available Automated Systems for Antibiotic Susceptibility Testing.
Review in Journal of tropical medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Integrating resistance biology, virulence networks, and precision therapeutics against multidrug-resistant Pseudomonas aeruginosa.Infection · 2026Review
- Rapid identification of carbapenem-resistant Acinetobacter baumannii based on MALDI-TOF mass spectrometry and machine learning.BMC microbiology · 2026Article
- Antibiotic resistance inFrontiers in microbiology · 2026Review
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
The battle against antibiotic resistance demands innovative solutions for efficient and accurate antibiotic susceptibility testing (AST). Traditional methods, though reliable, are often time-consuming and labor-intensive, limiting their ability to provide timely results in clinical settings. In response, automated systems have emerged as a revolutionary tool, offering rapid, precise, and comprehensive AST processes. The purpose of this review article was to provide a comprehensive guide on automated methods for AST, exploring their principles, advantages over traditional techniques, and implications for clinical practice. By examining recent advancements in this field, we highlight how these innovative approaches can revolutionize our ability to detect antibiotic resistance swiftly and accurately, ultimately improving patient outcomes and combating the growing threat of antimicrobial resistance. The findings from the collected studies indicate that employing various automated techniques for assessing antibiotic resistance facilitates the identification of resistance and allows for precise detection of different pathogens from infectious samples. These automated methods have the potential to decrease hospital stay duration and future treatment costs. Moreover, by accurately detecting resistance more quickly, they can lead to more targeted treatments, ultimately reducing patient mortality rates.
Indexed as
Identifiers
What Socratic 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 Socratic graph.