Fundamentals of artificial intelligence application and limitations of its evidence base in assisted reproductive technologies: an analytical review

Introduction. The development of artificial intelligence (AI) and machine learning methods opens up new opportunities for optimizing clinical and laboratory stages of assisted reproductive technology (ART) programs.

Aim: to evaluate the current level of efficiency, methodological quality, and implementation prospects of AI algorithms in the clinical and laboratory stages of ART programs.

Materials and Methods. A narrative literature review was conducted using PubMed/MEDLINE, eLibrary, and ScienceDirect databases covering the years 2016–2024 (additionally including fundamental 1996–2015 studies). The search was carried out by using Boolean operators: ("artificial intelligence" OR "machine learning" OR "deep learning") AND ("IVF" OR "assisted reproductive technology" OR "embryo selection"). A total of 60 publications containing verifiable quantitative algorithm performance metrics were included in the final analysis.

Results. AI use shows high accuracy in blastocyst segmentation and quality assessment (AUC = 0.86–0.91), as well as in predicting controlled ovarian hyperstimulation outcomes. However, most analyzed models are limited by single-center training datasets and a lack of external validation.

Conclusion. AI algorithms demonstrate high potential as decision support systems, but widespread implementation in ART practice requires prospective multicenter randomized controlled trials.

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