Artificial Intelligence in Total Laboratory Automation: Enhancing Quality, Accuracy, Reliability, and Timeliness Across the Total Testing Process
Artificial Intelligence, Total Laboratory Automation, Clinical Laboratory, Machine Learning, Laboratory Quality, Autoverification, Laboratory Medicine
AuthorsAbstractIn today's laboratory medicine, the use of artificial intelligence (AI) in conjunction with Total Laboratory Automation (TLA) is quickly making a difference. While clinical laboratories are being challenged by greater testing volume, reduced turnaround time, higher quality demands, and greater patient safety, they are also being impacted by the pressure of increasing volume. In addition to greater testing volume and reduced turnaround time, higher quality demands, and greater patient safety, clinical laboratories are being challenged by the pressure of increasing volume. While traditional automation has made it easier to cut down on manual actions, modern laboratory settings demand intelligent systems that can predictively analyze, autonomously make decisions, monitor quality continuously, and provide cutting-edge clinical support. AI technologies, such as machine learning, deep learning, computer vision, natural language processing, and predictive analytics, have become indispensable for solving these problems. AI use cases have been adopted across the entire testing workflow, from specimen collection to transportation, analytical testing, quality control, result verification, interpretation and reporting. These technologies have greatly helped to minimise laboratory errors, analytical performance, operational efficiency and evidence based clinical decision making. In addition, AIpowered systems enable predictive maintenance, automatic verification, risk management, and workflow optimization, amongst other benefits, and help ensure adherence to accreditation standards like ISO 15189 and CAP standards. This narrative review explores the role of AI in TLA systems today and the opportunities it could offer tomorrow, particularly in the context of laboratory quality, accuracy, reliability, timeliness and patient focused healthcare. Method: This study was intended as a narrative review that would give a full understanding of the present and upcoming uses of AI in Total Laboratory Automation (TLA). Literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar, as well as College of American Pathologists (CAP) guidelines and publications, Clinical and Laboratory Standards Institute (CLSI) guidelines and publications, International Organization for Standardization (ISO) publications, and U.S. Food and Drug Administration (FDA) publications. Articles written mainly between 2015 and 2025 in English language were taken into consideration, and landmark articles were added where appropriate. The literature retrieved was critically reviewed and organized thematically into pre-analytical, analytical, post-analytical, quality management, validation, regulatory and future applications of AI in laboratory medicine. Existing literature was reviewed to assess the current evidence, knowledge gaps and future directions for research. ••••••••••••••••••••••••••••••••
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