Artificial intelligence (AI) is profoundly reshaping laboratory medicine, evolving from an auxiliary tool into a critical support system for clinical decision⁃making. Machine learning and deep learning techniques have been integrated throughout the entire testing process, significantly enhancing efficiency and accuracy in sample collection, data integration, automated detection, and result interpretation, etc. This transformation is driving laboratory medicine from experience⁃based practice toward a data⁃driven paradigm. However, the current application of AI in laboratory medicine still faces multiple challenges, including inconsistent data quality, insufficient model interpretability, and misalignment with clinical needs. This paper reviews recent advances in AI applications in laboratory medicine, emphatically analyzes key technical challenges, and explores future innovation directions and development trends for AI in this field. 

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