Clinical Text Summarization

Clinical text summarization focuses on automatically generating concise and accurate summaries of medical records, aiming to alleviate clinician workload and improve patient care. Current research heavily utilizes large language models (LLMs), often adapting open-source architectures through fine-tuning and reinforcement learning techniques to achieve high-quality summaries comparable to those produced by medical experts, while addressing cost and privacy concerns. A key challenge lies in robustly evaluating these summaries, with ongoing work focusing on developing more reliable metrics and incorporating structured evaluation frameworks to ensure factual accuracy and clinical safety. This field holds significant promise for improving healthcare efficiency and potentially reducing medical errors through better information access and management.