Evidence map›Paper›PMID 41341692›Full record

ReviewJournal of tropical medicine2025

A Focused Review on Commercially Available Automated Systems for Antibiotic Susceptibility Testing.

Qumars Ghavami, Haider Abd Ulhai Nasser, Seyedeh Shadi Vaziri, Majid Taati Moghadam

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Antibiotic resistance inFrontiers in microbiology · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Qumars GhavamiDepartment of Microbiology, Faculty of Veterinary Science, Bu-Ali Sina University, Hamedan, Iran.
Haider Abd Ulhai NasserDepartment of Mathematics, Faculty of Basic Education, Babylon University, Babil, Iraq.
Seyedeh Shadi VaziriDepartment of Microbiology, Faculty of Basic Silences, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran.
Majid Taati MoghadamDepartment of Microbiology, Virology and Microbial Toxins, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.ORCID https://orcid.org/0000-0003-1557-3648

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

antibiotic resistanceantibiotic susceptibility testingautomated systems

Identifiers

PMID41341692
PMCPMC12672085

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.