The recent advancements in the medical AI field are significantly enhancing the capabilities of multimodal large language models (MLLMs) and large language models (LLMs) in various healthcare applications. These models are being fine-tuned and adapted to handle complex medical tasks such as diagnosis, documentation, and image segmentation with higher accuracy and efficiency. Notably, there is a strong emphasis on improving the interpretability and usability of these models, making them more accessible to medical professionals. Additionally, the integration of human-like interaction, such as gaze data for image segmentation, is demonstrating promising results in semi-supervised learning scenarios. The field is also witnessing innovations in data collection and model evaluation, with new benchmarks and datasets being introduced to better reflect real-world clinical capabilities. Furthermore, there is a growing focus on addressing the ethical, legal, and operational challenges associated with deploying LLMs in healthcare to ensure their responsible and effective integration. Overall, the direction of the field is towards more specialized, interactive, and reliable AI systems that can seamlessly integrate into clinical workflows, ultimately aiming to improve patient outcomes and healthcare efficiency.