MedGen: Unlocking Medical Video Generation by Scaling
  Granularly-annotated Medical Videos
Authors: name: Rongsheng Wang, name: Junying Chen, name: Ke Ji, name: Zhenyang Cai, name: Shunian Chen, name: Yunjin Yang, name: Benyou Wang

Recent advances in video generation have shown remarkable progress in
open-domain settings, yet medical video generation remains largely
underexplored. Medical videos are critical for applications such as clinical
training, education, and simulation, requiring not only high visual fidelity
but also strict medical accuracy. However, current models often produce
unrealistic or erroneous content when applied to medical prompts, largely due
to the lack of large-scale, high-quality datasets tailored to the medical
domain. To address this gap, we introduce MedVideoCap-55K, the first
large-scale, diverse, and caption-rich dataset for medical video generation. It
comprises over 55,000 curated clips spanning real-world medical scenarios,
providing a strong foundation for training generalist medical video generation
models. Built upon this dataset, we develop MedGen, which achieves leading
performance among open-source models and rivals commercial systems across
multiple benchmarks in both visual quality and medical accuracy. We hope our
dataset and model can serve as a valuable resource and help catalyze further
research in medical video generation. Our code and data is available at
https://github.com/FreedomIntelligence/MedGen