Clinical Note Summarization

Clinical note summarization aims to automatically condense lengthy medical records into concise, informative summaries, improving clinician efficiency and patient care. Current research heavily utilizes large language models (LLMs), often employing fine-tuning techniques and prompt engineering to enhance accuracy and faithfulness while mitigating issues like hallucinations and omissions. This field is crucial for reducing clinician workload, improving diagnostic accuracy, and facilitating better communication and decision-making within healthcare settings. A key focus is developing robust evaluation metrics that accurately capture the quality and factual accuracy of generated summaries.