Matrix spillover remains a challenging issue in flow cytometry analysis, influencing the reliability of experimental results. Recently, artificial intelligence (AI) have emerged as novel tools to mitigate matrix spillover effects. AI-mediated approaches leverage complex algorithms to quantify spillover events and correct for their impact on data in