Automation and artificial intelligence in the clinical microbiology laboratory
4 affiliations
- 1Laboratory Medicine Center, The Second Hospital & Clinical Medical School, Lanzhou University , , ,
- 2Department of Clinical Laboratory, Yumen City General Hospital (Yumen City First People’s Hospital) , , ,
- 3Department of General Surgery, The Second Hospital & Clinical Medical School, Lanzhou University , , ,
- 4Department of Laboratory Medicine, The First Hospital of Lanzhou University, The First School of Clinical Medicine of Lanzhou University , , ,
Abstract
Background: Clinical microbiology laboratories face persistent pressure to shorten turnaround times, reduce manual workloads, and standardize diagnostic workflows. Total laboratory automation (TLA) and artificial intelligence (AI) are complementary responses to these demands. In this review, we examine how the evidence base for automation and AI in clinical microbiology is distributed along a maturity gradient and what this distribution implies for the next phase of implementation.
Content: Available evidence indicates that automated platforms shorten culture turnaround times and reduce manual workload in observational and multi-center studies, although the scale of benefit depends on specimen type and workflow configuration. Automated reading, identification, and susceptibility testing systems showed high concordance with manual standards for common specimen types, with error rates below commonly cited acceptability thresholds. Evidence for AI performance in plate reading and resistance prediction is abundant; however, most models remain in an assistive phase without prospective validation. Randomized trials of rapid testing and automation rarely demonstrate overall clinical superiority, and patient-oriented benefits remain limited and inconsistent. Guidelines provide directional support, whereas interoperability, quality control, and regulatory frameworks are still under development.
Summary: The priority of the field is not additional single-center performance validation. The next phase requires multi-center prospective verification, interpretability, standardization, and comparative evidence with clinical outcomes as endpoints. AI-assisted evidence tools may accelerate evidence generation, although their utility remains preliminary.
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BibTeX
@article{wang2026automation,
title = {{Automation and artificial intelligence in the clinical microbiology laboratory}},
author = {Yaodong Wang and Yongxia Zhang and Na Li and Jinchun He},
journal = {Frontiers in cellular and infection microbiology},
year = {2026},
month = oct,
doi = {10.3389/fcimb.2026.2000316},
url = {https://europepmc.org/article/PMC/PMC13728431},
note = {CC BY},
}
RIS
TY - JOUR
TI - Automation and artificial intelligence in the clinical microbiology laboratory
AU - Yaodong Wang
AU - Yongxia Zhang
AU - Na Li
AU - Jinchun He
PY - 2026
DA - 2026/10/20
JO - Frontiers in cellular and infection microbiology
DO - 10.3389/fcimb.2026.2000316
UR - https://europepmc.org/article/PMC/PMC13728431
AB - Background: Clinical microbiology laboratories face persistent pressure to shorten turnaround times, reduce manual workloads, and standardize diagnostic workflows. Total laboratory automation (TLA) and artificial intelligence (AI) are complementary responses to these demands. In this review, we examine how the evidence base for automation and AI in clinical microbiology is distributed along a maturity gradient and what this distribution implies for the next phase of implementation. Content: Available evidence indicates that automated platforms shorten culture turnaround times and reduce manual workload in observational and multi-center studies, although the scale of benefit depends on specimen type and workflow configuration. Automated reading, identification, and susceptibility testing systems showed high concordance with manual standards for common specimen types, with error rates below commonly cited acceptability thresholds. Evidence for AI performance in plate reading and resistance prediction is abundant; however, most models remain in an assistive phase without prospective validation. Randomized trials of rapid testing and automation rarely demonstrate overall clinical superiority, and patient-oriented benefits remain limited and inconsistent. Guidelines provide directional support, whereas interoperability, quality control, and regulatory frameworks are still under development. Summary: The priority of the field is not additional single-center performance validation. The next phase requires multi-center prospective verification, interpretability, standardization, and comparative evidence with clinical outcomes as endpoints. AI-assisted evidence tools may accelerate evidence generation, although their utility remains preliminary.
SN - 2235-2988
ER -