The Role of Artificial Intelligence in Infertility Management: A Systematic Review of Current Applications and Future Directions

Background: Infertility affects 10–15% of reproductive-aged couples worldwide. Diagnostic variability, subjective interpretation, and heterogeneous treatment responses limit the effectiveness of conventional infertility management. Artificial intelligence (AI) has emerged as a promising adjunct in reproductive medicine.Objective: To systematically review the role of AI in infertility management, including diagnostic evaluation, assisted reproductive technology (ART), and outcome prediction.Methods: A systematic literature review was conducted using PubMed, Scopus, Web of Science, and Google Scholar databases (2015–2025). Keywords included artificial intelligence, machine learning, infertility, IVF, embryo selection, and reproductive imaging. Studies evaluating AI applications in male and female infertility were included. Data were synthesized narratively according to PRISMA guidelines.Results: A total of 112 studies met inclusion criteria. AI demonstrated high accuracy in ovarian reserve prediction, semen analysis, embryo selection, and IVF outcome prediction. Deep learning–based embryo assessment showed superior or non-inferior implantation prediction compared with expert embryologists. AI-assisted imaging reduced inter-observer variability and improved workflow efficiency.Conclusion: AI significantly enhances diagnostic precision, treatment personalization, and ART outcomes. Integration into infertility management should be guided by clinical validation, transparency, and ethical oversight.