Enhancing Quality in Laboratory Medicine through Machine Learning: A Comprehensive Review of Pre-Analytical, Analytical, and Post-Analytical Phases

Abstract

Background: The integration of machine learning (ML) within laboratory medicine is revolutionizing the quality of healthcare through improved diagnostic accuracy and operational efficiency. As laboratory testing is segmented into pre-analytical, analytical, and post-analytical phases, each phase presents unique challenges that can benefit from ML techniques. This review systematically assesses the application of ML in these phases to identify potential improvements in quality management and error reduction.

Methods: A comprehensive literature search was conducted using the PubMed database for studies published from 2000 to 2023, employing keywords such as “machine learning,” “laboratory medicine,” “biomarker,” and “laboratory test.”

Results: The findings reveal that ML algorithms significantly enhance specimen quality assurance in the pre-analytical phase, reduce operational costs and analytical errors during testing, and improve clinical decision-making in the post-analytical phase. Notably, studies indicated that ML outperformed traditional methods in detecting misidentification and sample quality issues, achieving accuracies exceeding 90%.

Conclusions: In conclusion, the deployment of machine learning in laboratory medicine offers substantial benefits across all testing phases, enhancing diagnostic precision and patient safety. The findings underscore the importance of integrating ML technologies into laboratory practices to facilitate better health outcomes. Future research should focus on refining these algorithms and exploring their application in diverse laboratory settings to maximize their potential.