The latest developments in biomedical multimodal models are pushing the boundaries of what is possible in medical image analysis and diagnosis. Researchers are increasingly focusing on integrating pixel-level insights and advanced multimodal capabilities to enhance the understanding and application of medical data. Key innovations include the introduction of models that support pixel-level prompts and grounding, enabling more precise and flexible interactions with medical images. Additionally, there is a growing emphasis on the use of unpaired multi-modal data to improve the practicality and applicability of these models in clinical settings. These advancements are not only improving diagnostic accuracy but also enhancing the efficiency and interpretability of medical image analysis, making them more suitable for real-world clinical use. Notably, models that leverage novel training strategies and comprehensive datasets are setting new benchmarks in performance, particularly in tasks such as visual question answering and report generation. These developments suggest a future where multimodal models will play a crucial role in advancing clinical pathology and retinal disease recognition.