Abstract

Artificial intelligence (AI) is progressively revolutionizing clinical and nonclinical laboratory settings by optimizing data management, improving diagnostic precision, and automating monotonous and time-intensive procedures. This extensive research assesses the present and developing uses of AI technologies—such as machine learning, robotic process automation (RPA), and natural language processing (NLP)—across several laboratory settings. Notable applications include the automation of sample preparation and analysis, real-time quality assurance, sophisticated pattern recognition in diagnostics, and the synthesis of intricate datasets for more personalised and data-driven insights.  The use of AI has resulted in significant enhancements in operational efficiency, accuracy, and the general speed of scientific advancement. It accelerates turnaround times, minimises human error, and allows labs to manage higher data volumes with enhanced consistency. Nonetheless, several obstacles impede the comprehensive use of AI in laboratory environments. This encompasses data privacy and cybersecurity threats, ethical issues related to algorithmic decision-making, opposition to advancements in technology, and substantial initial investment expenses. The research underscores the need for ongoing professional growth, interdisciplinary cooperation between laboratory scientists and AI specialists, and the establishment of explicit regulatory and ethical frameworks to resolve these difficulties. This paper emphasises the transformational influence of AI on laboratory processes and delineates practical solutions to address existing limits and foster sustainable innovation in clinical and research labs.

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