Role of Artificial Intelligence in Clinical Laboratory Workflow Optimization: Review

Abstract

Background: The integration of Artificial Intelligence (AI) in clinical laboratories has emerged as a transformative force, promising enhanced workflow optimization and improved patient outcomes. As clinical testing increasingly consolidates, the need for efficient data management and decision-making processes has become paramount.

Methods: This review analyzes various AI methodologies applied in clinical laboratories, focusing on their role in pre-analytical, analytical, and post-analytical phases. We examined advancements in Laboratory Information Systems (LIS) that facilitate the collection and integration of vast healthcare data, emphasizing the use of machine learning (ML) algorithms for predictive analytics and operational efficiency.

Results: Findings indicate that AI tools have significantly improved the accuracy of laboratory results, enhanced diagnostic stewardship, and optimized test requests. Notable applications include the auto-validation of results, identification of sample mix-ups through delta checks, and predictive modeling for patient monitoring. The review highlights successful case studies where AI integration has led to streamlined workflows and reduced turnaround times, ultimately benefiting patient care.

Conclusion: The implementation of AI in clinical laboratories is reshaping the landscape of laboratory medicine, enabling a shift from traditional practices to more dynamic, patient-centered approaches. Continuous advancements in AI technologies and data integration strategies are essential for overcoming existing challenges, such as data standardization and privacy concerns. Future efforts should focus on fostering collaboration between laboratory professionals and data scientists to maximize the potential of AI in enhancing clinical laboratory services.