Vision-language foundation models for medical imaging: a review of current practices and innovations.

ABSTRACT: Foundation models, including large language models and vision-language models (VLMs), have revolutionized artificial intelligence by enabling efficient, scalable, and multimodal learning across diverse applications. By leveraging advancements in self-supervised and semi-supervised learning, these models integrate computer vision and natural language processing to address complex tasks, such as disease classification, segmentation, cross-modal retrieval, and automated report generation. Their ability to pretrain on vast, uncurated datasets minimizes reliance on annotated data while improving generalization and adaptability for a wide range of downstream tasks. In the medical domain, foundation models address critical challenges by combining the information from various medical imaging modal

SUBMITTER: Ryu JS 

PROVIDER: S-EPMC12411343 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature