Recent developments in the field of Large Language Models (LLMs) highlight ongoing research aimed at enhancing their metacognitive abilities for medical decision-making. A study indicates that while LLMs perform well on medical board exams, they lack essential metacognition necessary for reliable medical reasoning. In contrast, reasoning models like o1 and o3 are showing promise in improving LLMs' medical applications. Microsoft has tested the o1-preview model on various medical question-answering tasks, achieving new state-of-the-art results. Furthermore, researchers are exploring Retrieval Augmented Generation (RAG) techniques to enhance LLM performance, including the integration of reasoning models and innovative frameworks like Search-o1, which combines search strategies with reinforcement learning. Other advancements include EdgeRAG, designed for efficient mobile use, and PipeRAG, which reportedly achieves a 2.6x speedup in generation latency while improving output quality. These efforts reflect a broader trend in the AI research community to optimize LLMs for specific domains and improve their reasoning capabilities.