AI-driven text mining of the female reproductive system: enabling multiscale biomedical modeling and personalized medicine

The female reproductive system, including the endometrium, placenta, ovary, cervix, and fallopian tube, plays a critical role in conception, implantation, and fetal development. Recent advances in bioengineered models such as organoids, organ-on-a-chip platforms, and 3D bioprinting have expanded experimental capabilities, however, the rapid growth of this field has resulted in a large and fragmented body of literature, limiting systematic integration and analysis. Here, we present an artificial intelligence (AI)-driven text mining framework to systematically map research trends in the female reproductive system. A total of 347 peer-reviewed articles were collected and analyzed. Abstracts were embedded using BioBERT to capture contextual biomedical semantics. Subsequently, unsupervised topic modeling was performed using BERTopic with UMAP-based dimensionality reduction and HDBSCAN clustering. This analysis identified 15 fine-grained subtopics, which were further consolidated into six major thematic categories. The results show that current research is mainly focused on endometrial receptivity and implantation, placental barrier function and maternal-fetal interface, and tissue regeneration and biofabrication. In contrast, integrated multi-organ modeling and translational validation remain relatively underexplored. Overall, this AI-driven framework provides a quantitative and scalable approach to organizing complex biomedical literature. The findings offer a structured overview of the field and highlight emerging directions for multiscale modeling and personalized reproductive medicine.