Matrix spillover remains a significant issue in flow cytometry analysis, influencing the reliability of experimental results. Recently, machine learning algorithms have emerged as potential tools to mitigate matrix spillover effects. AI-mediated approaches leverage advanced algorithms to identify spillover events and compensate for their consequenc