AI has emerged as a transformative force in clinical laboratories, enhancing diagnostic accuracy, operational efficiency, and patient care. This review article aims to explore the current state of AI in clinical laboratories, with an emphasis on emerging applications, benefits, challenges, and barriers in implementing this technology. This study used multiple databases including PubMed, MEDLINE, and Google Scholar for literature searches; the retrieved data were images, text, graph, or tabular data. In conclusion, although AI promises a lot towards improving laboratory practices, problems arise such as those related to dat a validation, quality control, and the manner of collaboration between AI and human expertise. Such a conclusion underscores the need for a partnership between AI technology and laboratory personnel to leverage the goodness of AI and mitigate its inherent challenges.

Keywords: Artificial Intelligence, Human-AI Collaboration, Clinical Laboratory, Machine Learning, Quality Control, Implementation Barriers, Laboratory Workflow Optimization