Imagine a doctor’s visit where the key medical insights are automatically summarized in real-time. This is the vision of the research paper, "Real-time Speech Summarization for Medical Conversations," which introduces a groundbreaking system for generating summaries from live medical dialogues. Unlike previous systems that constantly update summaries after each spoken word, this system generates a concise summary after every few sentences (about 30 seconds of conversation), providing digestible medical insights without overwhelming the user. This innovation enhances the patient experience and reduces computational costs. A key challenge in training real-time summarization models is the lack of suitable medical conversation data. The researchers address this gap by introducing VietMed-Sum, the first publicly available dataset of its kind, containing real and simulated Vietnamese medical conversations and their corresponding summaries. They also pioneered a cost-effective method for generating these summaries, combining the power of large language models (LLMs) like ChatGPT with human editing. This collaborative approach is significantly faster than traditional manual summarization while ensuring high quality. The research team tested several state-of-the-art summarization models on VietMed-Sum and found that ViT5 and ViT5-vietnews outperformed other models, demonstrating their strong potential for real-world applications. While these models achieved strong results, human evaluation highlighted the continued importance of human oversight to guarantee fluency and factual accuracy. This real-time summarization system is a giant leap towards enhancing medical communication and could revolutionize how medical information is processed and understood by both doctors and patients. Future research could explore multilingual capabilities and deeper integration with electronic health records, potentially leading to more personalized and effective healthcare.