A narrative review of foundation models for medical image segmentation: zero-shot performance evaluation on diverse modalities.

ABSTRACT:

Background and objective

Foundation models are deep learning models pretrained on extensive datasets, equipped with the ability to adapt to a variety of downstream tasks. Recently, they have gained prominence across various domains, including medical imaging. These models exhibit remarkable contextual understanding and generalization capabilities, spurring active research in healthcare to develop versatile artificial intelligence solutions for real-world clinical environments. Inspired by this, this study offers a comprehensive review of foundation models in medical image segmentation (MIS), evaluates their zero-shot performance on diverse datasets, and assesses their practical applicability in clinical settings.

Methods

A total of 63 studies on foundation models for MIS wer

SUBMITTER: Noh S 

PROVIDER: S-EPMC12209621 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature