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

Abstract

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.

Graphical abstract

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