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Although substantial progress has been made in the generation of 2D medical images, the synthesis of complex medical videos remains an unexplored area. The available literature on the generation of synthetic medical videos is minimal, highlighting a significant gap in this emerging area of research. This paper reviews the literature related to biomedical video synthesis using diffusion models and generative adversarial networks. The review aims to consolidate all relevant literature and highlight the different publicly available datasets, performance matrices, and the challenges associated with the generation of medical videos, along with some potential mitigation strategies. The findings of this review reveal that key challenges, such as maintaining temporal consistency, addressing computational inefficiencies, and overcoming data scarcity, are interconnected issues. Addressing these issues collectively is essential for the development of accurate and robust generative models tailored for medical video synthesis. The proposed potential mitigation strategies for the limitations of generative models in this review serve as a foundational resource for future research, aiming to enhance the reliability and applicability of generative AI models in clinical settings. These advances have the potential to significantly impact the domains of connected healthcare and personalized medicine by enabling the generation of realistic, high-quality medical video data that can enhance the training of diagnostic algorithms, improve the robustness of AI-assisted video interpretation, simulate disease progression or regression for more precise treatment planning, and support the development of personalized medicine techniques through enriched longitudinal data analysis.<\/jats:p>","DOI":"10.1007\/s10462-025-11394-5","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T03:41:13Z","timestamp":1761190873000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Generative AI for biomedical video synthesis: a review"],"prefix":"10.1007","volume":"58","author":[{"given":"Nahlah","family":"Algethami","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Talha","family":"Iqbal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ihsan","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"11394_CR1","doi-asserted-by":"publisher","unstructured":"Abaid A, Ali\u00a0Farooq M, Hynes N, Corcoran P, Ullah I (2024) Synthesizing CTA image data for type-b aortic dissection using stable diffusion models. 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