Artificial intelligence in in-vitro fertilization (IVF): A new era of precision and personalization in fertility treatments

Article


Olawade, D. B., Teke, J., Adeleye, K. K., Weerasinghe, K., Maidoki, M. and David-Olawade, A. C. 2025. Artificial intelligence in in-vitro fertilization (IVF): A new era of precision and personalization in fertility treatments. Journal of Gynecology Obstetrics and Human Reproduction. 54 (Art. 102903). https://doi.org/10.1016/j.jogoh.2024.102903
AuthorsOlawade, D. B., Teke, J., Adeleye, K. K., Weerasinghe, K., Maidoki, M. and David-Olawade, A. C.
Abstract

In-vitro fertilization (IVF) has been a transformative advancement in assisted reproductive technology. However, success rates remain suboptimal, with only about one-third of cycles resulting in pregnancy and fewer leading to live births. This narrative review explores the potential of artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance various stages of the IVF process. Personalization of ovarian stimulation protocols, gamete selection, and embryo annotation and selection are critical areas where AI may benefit significantly. AI-driven tools can analyze vast datasets to predict optimal stimulation protocols, potentially improving oocyte quality and fertilization rates. In sperm and oocyte quality assessment, AI can offer precise, objective analyses, reducing subjectivity and standardizing evaluations. In embryo selection, AI can analyze time-lapse imaging and morphological data to support the prediction of embryo viability, potentially aiding implantation outcomes. However, the role of AI in improving clinical outcomes remains to be confirmed by large-scale, well-designed clinical trials. Additionally, AI has the potential to enhance quality control and workflow optimization within IVF laboratories by continuously monitoring key performance indicators (KPIs) and facilitating efficient resource utilization. Ethical considerations, including data privacy, algorithmic bias, and fairness, are paramount for the responsible implementation of AI in IVF. Future research should prioritize validating AI tools in diverse clinical settings, ensuring their applicability and reliability. Collaboration among AI experts, clinicians, and embryologists is essential to drive innovation and improve outcomes in assisted reproduction. AI's integration into IVF holds promise for advancing patient care, but its clinical potential requires careful evaluation and ongoing refinement.

JournalJournal of Gynecology Obstetrics and Human Reproduction
Journal citation54 (Art. 102903)
ISSN2468-8495
2468-7847
Year2025
PublisherElsevier
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Digital Object Identifier (DOI)https://doi.org/10.1016/j.jogoh.2024.102903
Publication dates
Online27 Dec 2024
Publication process dates
Accepted26 Dec 2024
Deposited05 Mar 2025
Copyright holder© 2025 The Authors
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