Artificial Intelligence in Reproductive Medicine: Transforming Assisted Reproductive Technologies

Abstract

Question Asked How is artificial intelligence (AI) transforming assisted reproductive technologies (ART), particularly in vitro fertilization (IVF), and what are its clinical impacts and limitations? Background AI offers potential to address ART challenges, including high costs, variable success rates, and rising infertility. Applications in embryo selection, gamete assessment, and personalized protocols aim to enhance objectivity and outcomes. Literature Search A systematic review of peer-reviewed articles (2019–2025) was conducted, using terms such as “artificial intelligence” and “IVF.” Studies focused on AI tools (DeepEmbryo, icONE, iDAScore, ERICA) and their performance in ART. Materials and Methods Selected studies evaluated AI applications in embryo selection, gamete assessment, personalized protocols, and outcome prediction. Performance metrics, validation scope, and clinical outcomes were analyzed, prioritizing tools with quantitative data. Results and Discussion AI tools improved clinical pregnancy rates (up to 77.3%), implantation accuracy (92%), and efficiency (35%). icONE and ERICA outperformed traditional methods, reducing subjectivity. However, validation is often limited to single-center studies, with surrogate endpoints (e.g., pregnancy rates) rather than live birth rates. Algorithmic bias, regional data privacy regulations, and high costs limit generalizability and accessibility. Ethical concerns, including data privacy and equity, require robust frameworks. Conclusions AI enhances ART efficacy and personalization but faces validation and ethical challenges. Multicenter studies focusing on live birth rates and inclusive datasets are needed to ensure equitable, clinically relevant adoption.