Imagine a world where AI can quickly summarize complex medical texts, making information accessible to everyone. That's the promise of Large Language Models (LLMs). But with so many open-source LLMs emerging, how do you choose the right one for medical summarization? A new study tackles this exact question, comparing popular models like Llama2 and Mistral to see which excels at summarizing medical questions, research, and even patient-doctor dialogues. Researchers used a clever approach, pitting the open-source models against each other and using GPT-4 as the judge. The results? Llama2-70B consistently outperformed the others, showing a knack for summarizing complex medical information. While this research offers exciting possibilities, it also highlights the need for better evaluation methods. The team found that while GPT-4 is good at spotting inaccuracies, it's less sensitive to the length of summaries, which can be crucial in medical contexts. This research is a big step towards harnessing the power of LLMs in healthcare. Future work will focus on refining prompts, exploring more models, and addressing ethical considerations like data privacy and bias. As AI continues to evolve, we can expect even more powerful tools to help us navigate the complexities of medical information.