The recent advancements in the field of large language models (LLMs) have primarily focused on evaluating and enhancing their performance in specialized tasks, particularly in the medical domain. A significant trend observed is the development and use of benchmarks to assess LLMs' capabilities in clinical decision-making, knowledge infusion, and temporal generalization. These benchmarks, such as FineTuneBench, ClinicalBench, and Daily Oracle, aim to quantify the efficacy of LLMs in learning new information, updating existing knowledge, and predicting future events. The results from these studies indicate that while LLMs show promise, they still fall short in complex clinical tasks and dynamic knowledge updates. Notably, traditional machine learning models continue to outperform LLMs in clinical prediction tasks, emphasizing the need for further refinement and adaptation of LLMs for specific domains. Additionally, the adaptation of general-purpose LLMs and vision-language models for medical applications has shown limited impact, suggesting that current state-of-the-art models may already possess robust medical knowledge and reasoning capabilities. The field is moving towards more dynamic and user-adaptive summarization techniques, as evidenced by the introduction of Dynamic-granularity TimELine Summarization (DTELS), which addresses the need for flexible timeline summaries in rapidly evolving news environments. Overall, the research highlights the ongoing challenges in integrating LLMs into practical, high-stakes applications and underscores the importance of continuous evaluation and model updates to maintain performance and relevance.