AI/ML-enabled sepsis prediction systems: laboratory considerations from development to deployment

Sepsis prediction algorithms based on machine learning are increasingly embedded in electronic health record systems, and clinical laboratories have been largely absent from their development, validation, and governance. This is a consequential gap. Laboratory variables are central inputs to most deployed sepsis artificial intelligence (AI) tools, and routine analytical events such as platform transitions, reagent reformulations, and reference interval updates can shift the distributions of these inputs. Such distribution shifts are a recognized cause of degraded model performance; however, the effects of laboratory-specific changes remain insufficiently characterized for sepsis tools in clinical use. Neither clinical teams nor laboratories are currently well positioned to detect these effects. This review is written for clinical laboratorians who encounter these tools in practice or are asked to evaluate them for implementation. We describe the data sources used as model inputs and how analytical context, preanalytical variability, and ordering patterns influence model behavior. Five deployed algorithms with real-world implementation evidence are profiled alongside the broader preclinical landscape, including early warning systems, mortality and complication prediction models, and sepsis subtyping research. Development and implementation pitfalls are reviewed with attention to laboratory-specific considerations, and the regulatory and governance landscape is addressed with emphasis on where laboratorian involvement is most needed. Controlled outcomes data supporting currently deployed tools remain limited, and reported performance frequently does not replicate at external sites. Laboratorians bring expertise in analytical quality, data provenance, and change management that is not replicated elsewhere on implementation teams. As the field moves toward sepsis subtyping, continuous monitoring, and local model calibration, laboratorian expertise in analytical quality and monitoring will become increasingly central to how these tools are built, deployed, and maintained.