The field of medical research is witnessing significant advancements in the application of artificial intelligence, particularly in the development of large language models (LLMs) for medical education and diagnostic simulations. Recent innovations focus on creating interactive and dynamic environments that mimic real-world clinical scenarios, enabling the evaluation and enhancement of LLM performance in complex diagnostic settings. The integration of self-improvement mechanisms, multi-agent discussions, and chain-of-thought reasoning facilitates progressive learning and improves the accuracy of diagnostic interactions. Furthermore, the use of LLMs in medical education has the potential to revolutionize and modernize the field, providing scalable and interactive platforms for continuous learning and skill development. Noteworthy papers in this area include: MedAgentSim, which introduces an open-source simulated clinical environment for evaluating and enhancing LLM performance in dynamic diagnostic settings. MediTools, which leverages LLMs to develop interactive tools for medical education, including a dermatology case simulation tool and an AI-enhanced PubMed tool.