An Introduction to the Machine Learning Lifecycle for Clinical Microbiology.
TL;DR
This narrative review provides a structured introduction to the machine learning (ML) lifecycle from the perspective of clinical microbiology, outlining the sequence of steps in data preparation, model development, evaluation, and deployment.
Abstract
Background
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. SOURCES This narrative review synthesizes information from peer-reviewed literature in clinical microbiology and machine learning, including applied research articles and relevant guidelines on AI development, evaluation, and implementation in healthcare.
Objectives
This narrative review provides a structured introduction to the machine learning (ML) lifecycle from the perspective of clinical microbiology, outlining the sequence of steps in data preparation, model development, evaluation, and deployment. CONTENT We describe the characteristics of modern microbiology datasets and emphasize the importance of rigorous problem definition, data integration, quality assessment, and feature engineering. Model development considerations are summarized for supervised learning, including hyperparameter optimization, model choice, and multimodal data integration. Evaluation frameworks are examined with attention to typical challenges for microbiology applications, including class imbalance, generalization, robustness, model interpretation, and explainability. Finally, we summarize key elements of model deployment, including reproducible packaging, integration with Laboratory Information Systems and Electronic Medical Record systems, MLOps practices, ongoing drift monitoring, regulatory and governance requirements. IMPLICATIONS Successful AI implementation in microbiology demands alignment with laboratory workflows, transparency and interpretability of model behavior, robust performance under real-world variability, and strong data governance. Addressing these factors is essential for translating promising methodological advances into solutions to enhance diagnostics, antimicrobial stewardship, and infection prevention.