Explainable artificial intelligence for precision medicine in acute myeloid leukemia.

Journal: Frontiers in immunology
Published Date:

Abstract

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 tumor samples with 122 screened drugs and WES. MOM returned a therapeutic strategy based on the , , and 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.

Authors

  • Marian Gimeno
    Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián, Spain.
  • Edurne San José-Enériz
    Programa Hemato-Oncología, Centro de Investigación Médica Aplicada, Instituto de Investigación Sanitaria de Navarra (IDISNA), Universidad de Navarra, Pamplona, Spain.
  • Sara Villar
    Departamento de Hematología and CCUN (Cancer Center University of Navarra), Clínica Universidad de Navarra, Universidad de Navarra, Pamplona, Spain.
  • Xabier Agirre
    Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain.
  • Felipe Prosper
    Centro de Investigación Biomédica en Red de Cáncer, CIBERONC, Madrid, Spain.
  • Angel Rubio
    Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián, Spain.
  • Fernando Carazo
    Departamento de Ingeniería Biomédica y Ciencias, TECNUN, Universidad de Navarra, San Sebastián, Spain.