Matrix spillover remains a significant issue in flow cytometry analysis, influencing the precision of experimental results. Recently, machine learning algorithms have emerged as novel tools to mitigate matrix spillover effects. AI-mediated approaches leverage advanced algorithms to quantify spillover events and adjust for their impact on data inter