AUTHOR=Gimeno Marian , San José-Enériz Edurne , Villar Sara , Agirre Xabier , Prosper Felipe , Rubio Angel , Carazo Fernando 

TITLE=Explainable artificial intelligence for precision medicine in acute myeloid leukemia

JOURNAL=Frontiers in Immunology

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.977358

DOI=10.3389/fimmu.2022.977358

ISSN=1664-3224

ABSTRACT=<p>Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, the concept of “black box” in AI limits the potential of this approach to be translated into the clinical practice. In contrast, explainable AI (XAI) focuses on making AI results understandable to humans. Here, we present a novel XAI method -called multi-dimensional module optimization (MOM)- that associates drug screening with genetic events, while guaranteeing that predictions are interpretable and robust. We applied MOM to an acute myeloid leukemia (AML) cohort of 319 <italic>ex-vivo</italic> tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the <italic>FLT3</italic>, <italic>CBFβ-MYH11</italic>, and <italic>NRAS</italic> status, which predicted AML patient response to Quizartinib, Trametinib, Selumetinib, and Crizotinib. We successfully validated the results in three different large-scale screening experiments. We believe that XAI will help healthcare providers and drug regulators better understand AI medical decisions.</p>