Objective:To investigate the technical application pathways of artificial intelligence(AI)in laboratory information management systems(LIMS)and its role in promoting laboratory management efficiency and intelli-gence.Methods:Through the integration of traditional AI technologies(e.g.,machine learning,computer vision)with large language models,this study demonstrated the application of various AI technologies in scenarios such as intelligent Q&A for local knowledge bases,comprehensive review of inspection processes,intelligent data visualization,and image recognition.Results:Through the implementation of AI applications in laboratory settings,AI significantly enhanced management efficiency:the intelligent Q&A system achieved over 90％accuracy,auto-mated inspection processes reduced manual workload by 40％,and image recognition precision reached 89％-100％.Conclusion:AI provides efficient and precise solutions for laboratory management via multimodal integration and process optimization.Future efforts should focus on strengthening data security and model interpret-ability to promote comprehensive intelligent development.

Objective:To investigate the technical application pathways of artificial intelligence(AI)in laboratory information management systems(LIMS)and its role in promoting laboratory management efficiency and intelli-gence.Methods:Through the integration of traditional AI technologies(e.g.,machine learning,computer vision)with large language models,this study demonstrated the application of various AI technologies in scenarios such as intelligent Q&A for local knowledge bases,comprehensive review of inspection processes,intelligent data visualization,and image recognition.Results:Through the implementation of AI applications in laboratory settings,AI significantly enhanced management efficiency:the intelligent Q&A system achieved over 90％accuracy,auto-mated inspection processes reduced manual workload by 40％,and image recognition precision reached 89％-100％.Conclusion:AI provides efficient and precise solutions for laboratory management via multimodal integration and process optimization.Future efforts should focus on strengthening data security and model interpret-ability to promote comprehensive intelligent development.