  Medical image segmentation is a critical component in clinical practice,
facilitating accurate diagnosis, treatment planning, and disease monitoring.
However, existing methods, often tailored to specific modalities or disease
types, lack generalizability across the diverse spectrum of medical image
segmentation tasks. Here we present MedSAM, a foundation model designed for
bridging this gap by enabling universal medical image segmentation. The model
is developed on a large-scale medical image dataset with 1,570,263 image-mask
pairs, covering 10 imaging modalities and over 30 cancer types. We conduct a
comprehensive evaluation on 86 internal validation tasks and 60 external
validation tasks, demonstrating better accuracy and robustness than
modality-wise specialist models. By delivering accurate and efficient
segmentation across a wide spectrum of tasks, MedSAM holds significant
potential to expedite the evolution of diagnostic tools and the personalization
of treatment plans.
