ReviewFrontiers in cellular and infection microbiology2026
Evolving microbiology laboratories: mastering automated culture-based processes and molecular assays, an institutional experience.
Review in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Vancomycin-variable enterococci as a silent reservoir of resistance: characterization of the first reported vancomycin-variableFrontiers in microbiology · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Total laboratory automation (TLA) in microbiology integrates robotic specimen processing, automated conveyor systems, smart incubators, and high-resolution digital imaging to automate culture-based workflows from specimen setup to plate reading. Successful implementation requires careful planning, including assessment of existing laboratory infrastructure and a strategy for interfacing third-party instruments and information systems. Major barriers include capital investment, interoperability, and the need for standardized information technology interfaces. Recent advances in artificial intelligence (AI), particularly machine learning and convolutional neural networks, have extended the value of TLA by enabling automated image interpretation, culture plate screening, and predictive analyses. These tools can reduce manual workload and turnaround time while improving standardization. In this review, drawing primarily on our institutional experience, we examine the impact of TLA and AI on diagnostic microbiology workflows, implementation strategies, and performance assessment. We also discuss automated digital microscopy, the integration of phenotypic and molecular methods, and the principal limitations that still constrain broader adoption. Finally, we highlight the need for molecular diagnostic stewardship to preserve clinical relevance and cost-effectiveness.
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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.