Papers
1
Total Citations
3
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About
Dan Song is an emerging researcher whose work sits at the intersection of artificial intelligence and reproductive medicine, with a particular focus on obstetrics and gynecology. His most recognized contribution to date is a comprehensive bibliometric analysis examining global research trends in AI applications within obstetrics and gynecology spanning over two decades, from 1999 to 2025. Published in 2025, this study leverages Web of Science data to map the evolution of AI-driven innovations in reproductive healthcare, identify key research hotspots, and forecast future directions in this rapidly growing field. By synthesizing a broad landscape of scientific literature, Song's work provides the research community with a valuable navigational resource, helping clinicians and scientists understand where the field has been and where it is heading. Although his citation record is still developing — reflecting the recency of his publications — the interdisciplinary nature of his research positions him as a thoughtful contributor to the growing conversation around digital transformation in women's health. His efforts to bridge data science with clinical practice signal a promising trajectory for future impact in medical AI research.
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Top Papers
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