Recent studies have integrated artificial intelligence into reproductive medicine to improve ovarian reserve assessment and embryo classification. Hiroshi Koike's team applied advanced machine learning to evaluate ovarian reserve quantitatively and qualitatively, while research by M. Saraniya and J.A. Ruth introduced EmbryoNet-VGG16, combining deep learning with image segmentation to optimize embryo classification accuracy. These AI-driven tools aim to enhance fertility treatment outcomes by enabling precise evaluation and selection.