The recent surge in the availability of online videos has changed the way of acquiring information and knowledge. Many people prefer instructional videos to teach or learn how to accomplish a particular task in an effective and efficient manner with a series of step-by-step procedures. This need is not only limited to general audiences. In professional settings such as healthcare, instructional videos are widely used by physicians and other professionals to learn, review, and standardize procedural workflows. In addition, consumers increasingly seek step-by-step visual explanations to better understand medical procedures and clinical practices. With the advancement of generative models, the medical domain has also seen progress in medical video understanding, clinical decsion support etc,. Toward this, the MedGenVidQA shared task focuses on developing systems that utilize generative models to retrieve relevant multimodal (textual and visual) sources and to localize visual answers within medical videos in response to consumer and healthcare professional medical queries. Additionally, resource creation in the medical domain is both costly and time-consuming, as it often requires the involvement of medical experts. In this context, we also aim to assess the capability of generative models to create question–answer pairs from medical videos. Following earlier editions of medical question answering tasks: MedVidQA 2023, MedVidQA 2024, BioGen 2024, and BioGen 2025, this shared task expands medical video question answering for both professionals and consumers, with a focus on generative approaches to solving these tasks.