Matrix spillover remains a challenging 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 detect spillover events and adjust for their consequences on data i