Artificial Intelligence in Semen Analysis and Sperm Selection: Opportunities and Challenges

ABSTRACT Background Male factors contribute to approximately half of all cases of couple infertility, and semen analysis remains the cornerstone of the diagnostic evaluation. In recent years, artificial intelligence (AI) has emerged as a promising tool to improve the objectivity and reproducibility of semen analysis and sperm selection in assisted reproductive technologies (ART). Materials and Methods This narrative review provides an overview of the current applications of AI in seminology, focusing on sperm motility, morphology, DNA fragmentation, sperm selection for intracytoplasmic sperm injection (ICSI), and sperm retrieval in men with nonobstructive azoospermia. Beyond laboratory applications, the review also highlights the emerging role of machine learning in predicting reproductive outcomes and supporting clinical decision‐making in andrology. Results and Discussion Although the available evidence is encouraging, most studies remain limited by small and highly selected datasets, a lack of external validation, and scarce evidence on clinically meaningful outcomes. Future progress in the field will likely depend not only on improvements in algorithm performance, but also on the availability of high‐quality, representative datasets to enable robust validation and reliable clinical translation. While issues related to data quality, algorithmic bias, and regulatory and ethical aspects still need to be addressed, AI has the potential to become a valuable decision‐support tool in reproductive medicine. Conclusions Nonetheless, further prospective multicenter studies are needed before these technologies can be routinely integrated into clinical practice.