mlsanityData to 14 Oct 2026

Artificial Intelligence for Biomedical Video Generation

arXiv 2411.07619 ↗First posted 12 Nov 2024cs.CVcited by 2

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Abstract (arXiv)

As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies, significantly enhancing the quality of synthesized videos. Particularly in the realm of biomedicine, video generation technology has shown immense potential such as medical concept explanation, disease simulation, and biomedical data augmentation. In this article, we thoroughly examine the latest developments in video generation models and explore their applications, challenges, and future opportunities in the biomedical sector. We have conducted an extensive review and compiled a comprehensive list of datasets from various sources to facilitate the development and evaluation of video generative models in biomedicine. Given the rapid progress in this field, we have also created a github repository to regularly update the advances of biomedical video generation at: https://github.com/Lee728243228/Biomedical-Video-Generation

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 251 of its 353 references that are in the map. First posted on arXiv 12 Nov 2024.

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251 of 353 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Survey of Video Diffusion Models: Foundations, Implementations, and Applicationscited by 14
  2. 2026From Articulated Kinematics to Routed Visual Control for Action-Conditioned Surgical Video Generationcited by 4