Wu Yen-Chen, Chia-Yu Su Emily, Hou Jung-Hsiu, Lin Ching-Jung, Lin Krystal Baysan, Chen Chi-Huang
Division of Reproductive Medicine, Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei, Taiwan; Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.
Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.
Taiwan J Obstet Gynecol. 2025 Jan;64(1):11-26. doi: 10.1016/j.tjog.2024.10.001.
The objective of this review is to evaluate the contributions of Artificial Intelligence (AI) to Assisted Reproductive Technologies (ART), focusing on its role in enhancing the processes and outcomes of fertility treatments. This study analyzed 48 relevant articles to assess the impact of AI on various aspects of ART, including treatment efficacy, process optimization, and outcome prediction. The effectiveness of different machine learning paradigms-supervised, unsupervised, and reinforcement learning-in improving ART-related procedures was particularly examined. The findings indicate that AI technologies significantly enhance ART processes by refining tasks such as embryo and sperm analysis and facilitating personalized treatment plans based on predictive modeling. Notable improvements were observed in the accuracy of diagnosing and predicting successful outcomes in fertility treatments. AI-driven models provided more precise forecasts of the optimal timing for clinical interventions such as egg retrieval and embryo transfer, which are critical to the success of ART cycles. The integration of AI into ART represents a transformative advancement, substantially improving the precision and efficiency of fertility treatments. The continuous evolution of AI methodologies is likely to further revolutionize this field, enabling more tailored and successful treatment approaches. AI is becoming an indispensable tool in reproductive medicine, enhancing both the effectiveness of treatments and the clinical decision-making process. This review underscores the potential of AI to act as a catalyst for innovative solutions in the optimization of ART.
本综述的目的是评估人工智能（AI）对辅助生殖技术（ART）的贡献，重点关注其在改善生育治疗过程和结果方面的作用。本研究分析了48篇相关文章，以评估人工智能对辅助生殖技术各个方面的影响，包括治疗效果、流程优化和结果预测。特别考察了不同机器学习范式（监督学习、无监督学习和强化学习）在改善辅助生殖技术相关程序方面的有效性。研究结果表明，人工智能技术通过优化胚胎和精子分析等任务，并基于预测模型制定个性化治疗方案，显著改善了辅助生殖技术流程。在生育治疗的诊断准确性和成功结果预测方面观察到了显著改善。人工智能驱动的模型为取卵和胚胎移植等临床干预的最佳时机提供了更精确的预测，这对辅助生殖技术周期的成功至关重要。将人工智能整合到辅助生殖技术中代表了一项变革性进展，极大地提高了生育治疗的精度和效率。人工智能方法的不断发展可能会进一步彻底改变这一领域，实现更具针对性和更成功的治疗方法。人工智能正成为生殖医学中不可或缺的工具，提高了治疗效果和临床决策过程。本综述强调了人工智能作为辅助生殖技术优化创新解决方案催化剂的潜力。
