The field of medical imaging analysis is rapidly evolving with the integration of multimodal large language models (MLLMs). Recent research has focused on developing innovative architectures and frameworks that leverage the strengths of MLLMs to improve performance in various medical imaging tasks. A key direction in this field is the exploration of mixture-of-experts (MoE) paradigms, which enable dynamic expert selection and effective utilization of multi-scale visual features. This approach has shown promising results in medical image segmentation and 3D visual geometry reconstruction. Another area of research is the development of test-time model merging techniques, which aim to address the limitations of pretrained networks and fine-tuned expert models in medical imaging analysis. The use of MLLMs has also been extended to facial expression recognition, with the introduction of benchmarks and datasets that enable the evaluation of these models in this domain. Furthermore, the creation of large-scale datasets for text-guided medical image editing and comprehensive multimodal benchmarks for brain imaging analysis has facilitated the advancement of MLLMs in these areas. Noteworthy papers in this field include MoME, which proposes a mixture of visual language medical experts for medical image segmentation, and T3, which introduces a test-time model merging framework for zero-shot medical imaging analysis. Additionally, Fleming-VL presents a unified end-to-end framework for comprehensive medical visual understanding across heterogeneous modalities, and OmniBrainBench provides a comprehensive multimodal benchmark for brain imaging analysis. These advancements have the potential to significantly improve the accuracy and efficiency of medical imaging analysis, and pave the way for the development of more sophisticated and effective MLLMs in this field.