Medical Question Summarization

Medical question summarization aims to condense lengthy patient queries into concise, clinically relevant summaries, improving doctor-patient communication and medical decision-making. Recent research emphasizes multimodal approaches, incorporating both textual and visual (e.g., medical images) information to create richer, more informative summaries, often leveraging large language models (LLMs) and contrastive learning techniques to improve accuracy and focus on key medical entities. These advancements address challenges in capturing the core intent of patient questions and mitigating data biases, ultimately leading to more efficient and effective healthcare information processing.