Exploring the Role of Explainability in AI-Assisted Embryo Selection
Lucia Urcelay Daniel Hinjos Pablo A. Martin-Torres Marta Gonzalez Marta Mendez Salva Cívico Sergio Álvarez-Napagao Dario Garcia-Gasulla
In Vitro Fertilization is among the most widespread treatments for
infertility. One of its main challenges is the evaluation and selection of
embryo for implantation, a process with large inter- and intra-clinician
variability. Deep learning based methods are gaining attention, but their
opaque nature compromises their acceptance in the clinical context, where
transparency in the decision making is key. In this paper we analyze the
current work in the explainability of AI-assisted embryo analysis models,
identifying the limitations. We also discuss how these models could be
integrated in the clinical context as decision support systems, considering the
needs of clinicians and patients. Finally, we propose guidelines for the sake
of increasing interpretability and trustworthiness, pushing this technology
forward towards established clinical practice.