The Medical Segmentation Decathlon (MSD) is an international challenge designed to assess the generalizability of medical image segmentation algorithms. The challenge consists of ten different datasets, each with unique characteristics, and participants are required to develop a single algorithm capable of performing well on all tasks without human intervention. The MSD results confirmed that algorithms capable of performing well on multiple tasks generalize well to unseen tasks, as demonstrated by the continued strong performance of the winning algorithm, nnU-Net, in subsequent challenges over two years. The study highlights three main conclusions: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate for generalizability; and (3) the training of accurate AI segmentation models is now accessible to scientists without extensive AI expertise. The MSD dataset, which includes a wide range of clinical applications and challenging characteristics, has been widely used and is available under a permissive license, promoting its broader adoption in the biomedical image analysis community.The Medical Segmentation Decathlon (MSD) is an international challenge designed to assess the generalizability of medical image segmentation algorithms. The challenge consists of ten different datasets, each with unique characteristics, and participants are required to develop a single algorithm capable of performing well on all tasks without human intervention. The MSD results confirmed that algorithms capable of performing well on multiple tasks generalize well to unseen tasks, as demonstrated by the continued strong performance of the winning algorithm, nnU-Net, in subsequent challenges over two years. The study highlights three main conclusions: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate for generalizability; and (3) the training of accurate AI segmentation models is now accessible to scientists without extensive AI expertise. The MSD dataset, which includes a wide range of clinical applications and challenging characteristics, has been widely used and is available under a permissive license, promoting its broader adoption in the biomedical image analysis community.