The Role of Artificial Intelligence in Enhancing Quality Management Systems Within Clinical Laboratories: Implications for Diagnostic Accuracy and Patient Safety

Abstract

Background: The integration of artificial intelligence (AI) into clinical laboratories has emerged as a pivotal advancement aimed at improving diagnostic accuracy and patient safety. As healthcare systems grapple with increasing demands for precision medicine and efficient resource allocation, AI technologies offer innovative solutions to enhance quality management systems (QMS). This study systematically reviews the existing literature on AI applications in clinical laboratories, focusing on their impact on diagnostic processes, error reduction, and overall patient outcomes.

Methods: A comprehensive search was conducted across multiple databases, including PubMed, Scopus, and Web of Science, yielding studies published up to 2023.

Results: The analysis revealed that AI-driven algorithms significantly enhance the interpretation of laboratory results, optimize workflow efficiencies, and reduce diagnostic errors. Key findings indicate that AI applications, such as machine learning and predictive analytics, markedly improve the accuracy of test interpretations and facilitate timely decision-making in clinical settings.

Conclusions: In conclusion, the adoption of AI technologies within quality management systems in clinical laboratories is crucial for advancing diagnostic accuracy and ensuring patient safety. This study underscores the need for healthcare policymakers and laboratory administrators to invest in AI-driven solutions that can streamline operations and enhance the quality of laboratory services. Future research should focus on developing standardized protocols for AI implementation and assessing long-term impacts on patient care and safety.