The application of visual-language large models in the field of medical health has gradually become a research focus. The models combine the capability for image understanding and natural language processing, and can simultaneously process multi-modality data such as medical images and medical reports. These models can not only recognize images, but also understand the semantic relationship between images and texts, effectively realize the integration of medical information, and provide strong support for clinical decision-making and disease diagnosis. The visual-language large model has good performance for specific medical tasks, and also shows strong potential and high intelligence in the general task models. This paper provides a comprehensive review of the visual-language large model in the field of medical health. Specifically, this paper first introduces the basic theoretical basis and technical principles. Then, this paper introduces the specific application scenarios in the field of medical health, including modality fusion, semi-supervised learning, weakly supervised learning, unsupervised learning, cross-domain model and general models. Finally, the challenges including insufficient data, interpretability, and practical deployment are discussed. According to the existing challenges, four potential future development directions are given.

Deep learning technology has been widely used in brain tumor segmentation with multi-modality magnetic resonance imaging, helping doctors achieve faster and more accurate diagnoses. Previous studies have demonstrated that the weighted fusion segmentation method effectively extracts modality importance, laying a solid foundation for multi-modality magnetic resonance imaging segmentation. However, the challenge of fusing multi-modality features with single-modality features remains unresolved, which motivated us to explore an effective fusion solution. We propose a multi-modality and single-modality feature recalibration network for magnetic resonance imaging brain tumor segmentation. Specifically, we designed a dual recalibration module that achieves accurate feature calibration by integrating the complementary features of multi-modality with the specific features of a single modality. Experimental results on the BraTS 2018 dataset showed that the proposed method outperformed existing multi-modal network methods across multiple evaluation metrics, with spatial recalibration significantly improving the results, including Dice score increases of 1.7%, 0.5%, and 1.6% for the enhanced tumor core, whole tumor, and tumor core regions, respectively.