The Iranian Journal of Obstetrics, Gynecology and Infertility

The Iranian Journal of Obstetrics, Gynecology and Infertility

Applications of Artificial Intelligence in Diagnosis and Treatment of Infertility: A Narrative Review

Document Type : Original Article

Authors
Malihe Afiat 1
Dina Abadi Bavil 2
1 Associate Professor, Department of Obstetrics and Gynecology, Family and Youth of Population Support Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
2 PhD Student in Reproductive Health, Nursing and Midwifery Care Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. Department of Nursing and Midwifery, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
10.22038/ijogi.2026.91746.6569
Abstract
Introduction: Along with the growth of modern technologies, the use of artificial intelligence (AI) in infertility diagnosis and treatment has expanded rapidly; however, the available evidence is scattered and heterogeneous, and most studies have not provided a comprehensive systematic review. Previous studies have shown that AI models have promising performance than traditional methods in predicting IVF success, assessing embryos, and analyzing sperm; however, results have been inconsistent, necessitating a systematic review. This study aims to systematically review the applications of AI in the field of infertility and to analyze the accuracy, benefits, and limitations of this technology.
Methods: In this narrative review which was conducted in accordance with PRISMA 2020 guidelines, a search was performed in PubMed, Scopus, Web of Science, Google Scholar, and Cochrane databases between 2015 and 2025. Out of 4,320 retrieved records, 70 studies met the inclusion criteria after screening and quality appraisal using the QUADAS-2 tool. The data were synthesized narratively.
Results: Artificial intelligence models have demonstrated considerable effectiveness in various areas of infertility. In predicting the success of in vitro fertilization (IVF), the accuracy of machine learning–based models has been reported to range between 75% and 92% in different studies. In addition, in the assessment of embryo quality using time‑lapse imaging, deep learning models in some cases have shown better performance than human evaluation. In sperm analysis, computer vision algorithms have also demonstrated greater accuracy than manual methods in detecting morphological abnormalities and DNA fragmentation. Furthermore, machine learning models have shown promising results in personalizing treatment protocols, particularly in determining gonadotropin dosage and predicting ovarian response. In the diagnosis of causes of female infertility, the accuracy of artificial intelligence models in identifying conditions such as polycystic ovary syndrome (PCOS) and certain hormonal disorders has been reported to range between 70% and 88%.
Conclusion: AI can significantly enhance diagnostic accuracy, improve embryo selection, and enable personalized reproductive care. Nevertheless, challenges remain, including data bias, lack of standardized datasets, and limited external clinical validation. Future research should focus on improving algorithm transparency and developing more reliable models.
Keywords
Artificial Intelligence
Assisted Reproductive Technology
Embryo Selection
Infertility
IVF
Machine Learning
Subjects
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The Iranian Journal of Obstetrics, Gynecology and Infertility
Volume 29, Issue 1
March 2026
Pages 65-80