Recently, the advent of multimodal large language models (MLLMs) which integrate text with other modalities such as images, audio, and video, promise to revolutionize various fields ranging from natural language processing to computer vision and beyond. However, while MLLMs have shown remarkable performance across a wide range of tasks, their inner workings remain largely opaque, presenting significant challenges in terms of interpretability, robustness, and ethical considerations. This paper investigates the next frontier in artificial intelligence research: understanding multimodal large language models. We explore the architecture and applications of MLLMs, shedding light on their capabilities and limitations. By delving into the intricacies of multimodal large language models, this paper aims to show the potential use of LLM in biomedical and advanced machine learning algorithms to extract valuable features and improve the prediction accuracy of clinical analysis. Therefore, it will pave the way for future research directions and facilitate the development of more transparent, equitable, and trustworthy AI systems.