Zhang Qing, Liang Xiaowen, Chen Zhiyi
Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China.
Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China.
J Assist Reprod Genet. 2025 Jan;42(1):3-14. doi: 10.1007/s10815-024-03284-6. Epub 2024 Oct 14.
The field of reproductive medicine has witnessed rapid advancements in artificial intelligence (AI) methods, which have significantly enhanced the efficiency of diagnosing and treating reproductive disorders. The integration of AI algorithms into the in vitro fertilization (IVF) has the potential to represent the next frontier in advancing personalized reproductive medicine and enhancing fertility outcomes for patients. The potential of AI lies in its ability to bring about a new era characterized by standardization, automation, and an improved success rate in IVF. At present, the utilization of AI in clinical practice is still in its early stages and faces numerous ethical, regulatory, and technical challenges that require attention. In this review, we present an overview of the latest advancements in various applications of AI in IVF, including follicular monitoring, oocyte assessment, embryo selection, and pregnancy outcome prediction. The aim is to reveal the current state of AI applications in the field of IVF, their limitations, and prospects for future development. Further studies, which involve the development of comprehensive models encompassing multiple functions and the conduct of large-scale randomized controlled trials, could potentially indicate the future direction of AI advancements in the field of IVF.
生殖医学领域见证了人工智能（AI）方法的迅速发展，这些方法显著提高了生殖疾病的诊断和治疗效率。将人工智能算法整合到体外受精（IVF）中，有可能成为推进个性化生殖医学和提高患者生育成功率的下一个前沿领域。人工智能的潜力在于它能够带来一个以标准化、自动化和提高体外受精成功率为特征的新时代。目前，人工智能在临床实践中的应用仍处于早期阶段，面临着众多需要关注的伦理、监管和技术挑战。在这篇综述中，我们概述了人工智能在体外受精各种应用中的最新进展，包括卵泡监测、卵母细胞评估、胚胎选择和妊娠结局预测。目的是揭示人工智能在体外受精领域的应用现状、局限性以及未来发展前景。进一步的研究，包括开发涵盖多种功能的综合模型和进行大规模随机对照试验，可能会指明人工智能在体外受精领域的未来发展方向。
