Artificial Intelligence in Embryo Selection What Every Reproductive Specialist Should Know

Abstract

Background: Artificial intelligence (AI) is rapidly becoming an important component of modern in vitro fertilization (IVF), offering opportunities to standardize embryo assessment, reduce subjectivity, and efficiently analyze static and time-lapse imaging data. Methods: A focused narrative review synthesized 17 publications from 2021 through 2025 on AI-assisted embryo ranking, viability and euploidy prediction, workflow optimization, quality assurance, and implementation: 1 randomized clinical trial, 1 systematic review, 2 conference abstracts, and 13 other journal publications. Four named embryo-assessment systems were profiled. Separately, published aggregate GGRC data were synthesized to illustrate a practice-based application of AI in IVF laboratory quality management. Results: ERICA, iDAScore, FiTTE, and IVFvision. ai provided automated embryo ranking or outcome prediction. Most supporting evidence was retrospective or simulation-based. In the only included multicenter randomized trial (n = 1,066), iDAScore did not establish noninferiority to standard morphology-based selection for clinical pregnancy; however, embryo assessment was approximately tenfold faster. The GGRC evidence demonstrated a complementary quality-management application rather than image-based embryo ranking: a KPI-based clinical pregnancy model was externally validated using 3,888 Georgian treatment cycles within a two-center dataset of 10,128 cycles, yielding a mean AUC of 0. 73. In Q2 2024, predicted and observed clinical pregnancy rates were 58. 9% and 59. 1%, and no statistically significant between-embryologist differences were detected. These findings support AI-assisted calibration and laboratory audit but do not demonstrate improved pregnancy or live-birth outcomes. Conclusion: AI’s strongest current contribution is human-supervised decision support and laboratory governance. The GGRC experience demonstrates practical value for calibration, KPI surveillance, troubleshooting, and targeted audits, but it does not yet prove that AI increases pregnancy or livebirth rates. Thoughtfully implemented AI can strengthen standardization, workflow, training, quality control, and evidence-based IVF care. However, it should not replace embryologists or be presented as a guarantee of euploidy, pregnancy, or live birth.