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.

本文分析了AI辅助胚胎分析模型的可解释性工作，确定了其局限性，并讨论了如何将这些模型整合到临床环境中作为决策支持系统，考虑了临床医生和患者的需求。最后，我们提出了增加解释性和可信度、推动这项技术走向临床实践的准则。

AI辅助胚胎筛选中可解释性的探索