As the global population continues to grow and undergo structural changes, humanity is faced with an ever-increasing array of health challenges.  Among these, reproductive health issues hold profound significance, as they not only impact individual quality of life but also bear heavily on societal sustainability.  Recently, infertility rates have been on the rise, and cancer within the reproductive system remains a major global health threat due to its high incidence and mortality rates. In the field of pathology, accurate analysis of tissue slices and cytological samples is crucial for disease diagnosis. However, this process is often time-consuming, labor-intensive, and prone to human-induced diagnostic errors. Furthermore, diagnosing complex diseases frequently requires collaboration among multiple experts. Yet, the uneven distribution of medical resources prevents many patients from accessing timely and high-quality pathological diagnosis services.  Artificial intelligence (AI) is poised to transform reproductive healthcare by conducting advanced analysis of diverse clinical data, such as longitudinal medical records, multimodal imaging, and multi-omics profiles.  The emergence of multimodal foundation models and large language models has enabled the cross-domain integration of diagnostic data, including text, imaging, and genomics.  This integration significantly enhances the precision of detecting complex pathologies and holds great promise for addressing the pressing challenges in reproductive health diagnosis.

Topics of interest are listed below, but not limited to, so articles, including research papers or surveys, related to these areas are also welcome.

• Sperm imaging analysis
• Intelligent reproductive medicine decision-making system
• Multi-source medical data diagnosis
• Pathology analysis based on statistics or machine learning
• (Multimodal) large language models for pathology or reproductive diagnosis
• Clinical diagnostic systems for reproductive medicine or pathology