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The article discusses the concept of foundation models (FMs) in surgical artificial intelligence (AI). FMs are trained on broad datasets and can be adapted to various surgical tasks, potentially revolutionizing the field of surgical AI. The article highlights the challenges in developing a surgical FM, such as the need for large volumes of data and computing power, as well as the contextual nature of surgery. It also explores potential applications of FMs in surgery, including risk prediction and anatomical segmentation. The article emphasizes the importance of validation, ethical considerations, and collaboration in the development of FMs. [Extracted from the article]