Exploring the Role of Explainability in AI-Assisted Embryo Selection

TL;DR

This paper reviews the importance of explainability in AI models for embryo selection in IVF, highlighting current limitations and proposing guidelines to improve interpretability and clinical trust.

Contribution

It provides an analysis of existing explainability methods in AI-assisted embryo analysis and offers guidelines to enhance transparency and clinical adoption.

Findings

Current AI models lack sufficient interpretability for clinical use

Proposed guidelines aim to improve model transparency and trustworthiness

Discussion on integrating AI as decision support in IVF clinics

Abstract

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.