A Machine Learning–Driven Framework for Adaptive Quality Assurance in Clinical Laboratory Systems: A Reconstructed Study
AUTHORS
Lukas Reinhardt,Department of Medical Informatics and Artificial Intelligence, Technical University of Munich, Germany
Anja Keller,Department of Medical Informatics and Artificial Intelligence, Technical University of Munich, Germany
Sophie Hartmann,Department of Medical Informatics and Artificial Intelligence, Technical University of Munich, Germany
ABSTRACT
This study proposes a machine learning–enabled, ICT-based quality assurance framework for clinical pathology laboratories, aimed at transforming conventional quality control from a reactive process into a predictive, adaptive system. While existing laboratory quality assurance practices rely heavily on rule-based validation and manual intervention, they remain limited in their ability to leverage accumulated data to prevent errors and optimize systems proactively. To address these limitations, the present work develops a modular and interoperable control architecture grounded in the medical software lifecycle standard IEC 62304 and augmented by agile development principles. The framework integrates multiple machine learning techniques—including Random Forest, XGBoost, LightGBM, and deep neural networks—to enable real-time anomaly detection, predictive equipment monitoring, and intelligent decision support. Empirical evaluation demonstrates that the proposed system achieves high accuracy in rule-based validation, supports multi-protocol communication, and ensures database interoperability across heterogeneous environments. Furthermore, real-time notification mechanisms and modular user interface configurations contribute to improved operational efficiency and user satisfaction. Beyond technical performance, this study identifies critical gaps in current laboratory information systems, particularly the underutilization of longitudinal quality control data and the lack of integrated predictive analytics. By addressing these gaps, the proposed approach offers a scalable solution to enhance diagnostic reliability, reduce operational costs, and support data-driven clinical decision-making—especially in resource-constrained medical settings.
KEYWORDS
Machine learning, Clinical laboratory systems, Quality assurance, Predictive analytics, Laboratory information systems, Real-time monitoring
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