Digital twins in fertility, assisted reproductive technology and pregnancy: a systematic review

Digital twins - the term for virtual representations of biological systems - are emerging as promising tools in reproductive medicine. They offer personalized simulations for optimizing fertility, assisted reproductive technology (ART) and pregnancy outcomes. However, their use remains limited and fragmented across diverse applications. A systematic search was conducted in PubMed, EMBASE, Scopus and IEEE Xplore up to July 2025 for this review of the current evidence on digital twins in fertility, ART and pregnancy, identifying applications, outcomes, challenges and future prospects. Original studies that applied digital twins to fertility, ART or pregnancy in human or in-silico models were included in this review. Eight original studies were included, complemented by nine mechanistic or conceptual works. Applications encompassed embryo selection, IVF procedure modelling, placental physiology, pregnancy pharmacokinetics, and intrapartum monitoring. Most studies were predictive or descriptive in nature, static or batch-coupled, and at early stages of validation. Risk of bias ranged from moderate to high due to study design and external validity concerns. Only two studies fulfilled strict digital twin criteria, and the exclusion of borderline studies did not change the overall conclusions. Digital twins hold substantial promise for personalized reproductive care. However, their clinical utility remains largely theoretical. Future work must improve modelling accuracy, data integration and ethical implementation to unlock their full potential.