aceAn Introduction to the Machine Learning Lifecycle for Clinical Microbiology
Clinical microbiology is undergoing rapid transformation driven by modern technologies generating high-volume, high-dimensional, and heterogeneous datasets that exceed the analytical capabilities of traditional rule-based approaches. Artificial intelligence (AI) provides powerful computational methods to gain diagnostic, biological, and epidemiological insights from these complex data. This narrative review synthesizes information from peer-reviewed literature in clinical microbiology and machine learning, inclu…
AI provides powerful computational methods to extract diagnostic, biological and epidemiological insights from high-volume microbiology datasets, with potential to enhance diagnostics, antimicrobial stewardship and infection prevention when integrated into lab workflows.
ML applications in clinical microbiology face typical challenges including class imbalance, limited generalization, robustness issues, and need for model interpretation and explainability to achieve robust performance under real-world variability.
Successful translation requires alignment with laboratory workflows, transparency and interpretability, robust performance under real-world variability, and strong data governance, with ongoing challenges around class imbalance and generalization.
Evidence
- Peer-reviewedClinical Microbiology and Infection2026-08-15
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Truvace Impact Record TRV-2026-0800, v1: “An Introduction to the Machine Learning Lifecycle for Clinical Microbiology.” Truvace, 2026-08-17. /record/TRV-2026-0800 (accessed at citation time). sha256 b3f88eb839b972f6…
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