Current state of artificial intelligence applications in assisted reproductive technology: A narrative review - Ju Hee Kim (Clinical and Experimental Reproductive Medicine, 2026)
Artificial intelligence (AI) has rapidly advanced in healthcare, demonstrating significant potential in analyzing large, heterogeneous datasets using optimized algorithms for disease prediction and personalized treatment. Assisted reproductive technology (ART), particularly in vitro fertilization (IVF) and embryo transfer, generates extensive data, making it especially suitable for AI-driven analysis. AI-based applications aim to improve clinical outcomes through personalized ART strategies and predictive algorithms, with potential applications categorized into various procedural stages. Despite its promising nature, most AI-related ART studies appear in general scientific journals rather than core obstetrics and gynecology publications. Moreover, limited clinician understanding of AI methodologies, strengths, and limitations represents a barrier to clinical implementation. This review summarizes recent advancements in AI applications within ART, covering areas such as clinical counseling, outcome prediction, IVF workflow management, controlled ovarian stimulation and follicular monitoring, oocyte and semen analysis, and embryo assessment. It also addresses future considerations for the responsible integration of AI technologies in ART, emphasizing the importance of multidisciplinary collaboration. Integrating AI into ART holds substantial promise and, with targeted research and development, is expected to meaningfully advance the achievement of successful pregnancies.