Exploration of the Clinical Application of Multimodal Artificial Intelligence in Sperm Screening: Identifying Opportunities and Addressing Challenges

One limitation of Assisted Reproductive Technology (ART) is its relatively low pregnancy success rate per cycle. A key contributing factor to this issue is the lack of precise and efficient sperm selection methods. The emergence of multimodal artificial intelligence (AI), which integrates diverse data types—including imaging, genomics, and clinical parameters—represents a transformative advancement in reproductive medicine. This system enhances the accuracy and comprehensiveness of sperm screening through dynamic functional assessments, molecular feature recognition, and advanced data integration strategies, thereby enabling the identification of sperm with high genomic integrity. This review explores recent innovations in multimodal AI for sperm screening, highlighting its potential to overcome the inherent limitations of traditional static morphological assessments. Furthermore, this paper addresses significant challenges, including data heterogeneity, model interpretability, and barriers to clinical translation. By systematically integrating machine learning, deep learning, and explainable AI techniques, multimodal AI offers promising strategies to improve ART outcomes through precise and comprehensive sperm analysis.