Medical Dialogue Summarization

Medical dialogue summarization aims to automatically generate concise and informative summaries of doctor-patient conversations, improving efficiency and accessibility in healthcare. Current research focuses on enhancing robustness to errors from automatic speech recognition (ASR) systems, often using large language models (LLMs) for data augmentation or in-context learning, and incorporating multimodal information (e.g., visual cues) and external medical knowledge into summarization models. These advancements are crucial for improving the accuracy and reliability of automated medical reporting, ultimately assisting healthcare professionals and potentially reducing the burden of manual documentation.