BiomedClip - Citation Ripples

If you’ve ever wondered how a research paper’s ideas ripple across disciplines, Litstudy[1] is the tool that brings those connections to life. Rather than simply tallying up citations, Litstudy dives deeper showing you exactly which domains have picked up on a paper’s concepts. To make the experience even more interactive, I’ve included a Python notebook in HTML format by taking the BiomedCLIP [2] paper as an example. BiomedClip Model is featured in AI Kosh, the Government of India’s AI repository [3].

This blog post highlights the application of BiomedCLIP's concepts in biomedical AI, emphasizing multimodal learning that combines image and text data—insights gathered from its references on arxiv.org. The influence is clear in areas like vision-language processing, medical image classification, visual question answering, and radiology.

But Litstudy doesn’t stop at just mapping citations—it also brings powerful topic modeling into the mix. With this feature, you can uncover the main themes and research trends that emerge from a paper’s citation network. Topic modeling automatically groups related citations, helping you visualize which areas of research are most influenced by the paper and how its ideas have evolved across disciplines. More specifically, one can narrow down to specific documents addressing a particular topic.

This means you’re not only seeing where a paper has been cited, but also gaining a deeper understanding of the conversations and innovations it has sparked.

Combined with the interactive Python notebook, Litstudy empowers you to explore these topics hands-on, making your literature reviews and blog posts richer, more insightful, and truly data driven. The following is the Jupyter Notebook:

A Jupyter Notebook with self-explanatory features and a Litstudy API to access arxiv.org

References

1. S. Heldens, A. Sclocco, H. Dreuning, B. van Werkhoven, P. Hijma, J. Maassen & R.V. van Nieuwpoort (2022), "litstudy: A Python package for literature reviews", SoftwareX, 20, 101207. DOI: 10.1016/j.softx.2022.101207

2. BiomedCLIP: A Multimodal Biomedical Foundation Model Pretrained from Fifteen Million Scientific Image–Text Pairs ; Sheng Zhang, Yanbo Xu, Naoto Usuyama, Hanwen Xu, Jaspreet Bagga, Robert Tinn, Sam Preston, Rajesh Rao, Mu Wei, Naveen Valluri, Cliff Wong, Andrea Tupini, Yu Wang, Matt Mazzola, Swadheen Shukla, Lars Liden, Jianfeng Gao, Angela Crabtree, Brian Piening, Carlo Bifulco, Matthew P. Lungren, Tristan Naumann, Sheng Wang, Hoifung PoonYear: 2025 Published on: arXiv:2303.00915

3. AIKosh