Automation and artificial intelligence in the clinical microbiology laboratory

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.

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.

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.