# Exploring the Role of Explainability in AI-Assisted Embryo Selection

arXiv: https://arxiv.org/abs/2308.02534 · HTML: https://mlsanity.com/paper/2308.02534/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks](https://mlsanity.com/paper/1710.11063/) (2017)
- [Attention Branch Network: Learning of Attention Mechanism for Visual Explanation](https://mlsanity.com/paper/1812.10025/) (2018)
- [Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative](https://mlsanity.com/paper/2103.04725/) (2021)
- [Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality](https://mlsanity.com/paper/2005.10912/) (2020)
- [Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization](https://mlsanity.com/paper/2105.00060/) (2021)
- [Comparison of attention models and post-hoc explanation methods for embryo stage identification: a case study](https://mlsanity.com/paper/2205.06546/) (2022)

## Built on by (most cited first, 1 of 1)

- [Merging synthetic and real embryo data for advanced AI predictions](https://mlsanity.com/paper/2412.01255/) (2024)

## Abstract (arXiv)

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.