用大模型自动生成出院小结，效果优于人工撰写。
Large Language Model-Based Generation of Discharge Summaries
- 对比5个大模型，用单次提示生成摘要。
- 谷歌Gemini表现最佳，与标准摘要相似度最高。
- 开源模型易幻觉，适合有隐私保障的场景。
出院小结是医疗人员记录患者就医过程的重要文档，包含大量关键信息。自动化生成可减轻医生负担、减少错误并提升信息可及性。本研究评估了五种大语言模型（Mistral、Llama 2、GPT-3、GPT-4、Gemini 1.5 Pro）在该任务中的表现，基于MIMIC-III数据集的病历文本与摘要。采用精确匹配、软重叠和无参考指标进行评估。结果表明，专有模型（尤其是Gemini在单次提示下）表现最优，生成摘要与标准答案相似度最高；开源模型虽具潜力（如微调后的Mistral），但普遍存在幻觉和重复问题。临床专家的人工评估确认了专有模型生成摘要的实用性。尽管存在幻觉与信息缺失等挑战，研究显示大模型（尤其专有模型）在确保数据隐私的前提下，是自动出院小结生成的有力候选方案。
原文摘要 · Abstract (English)
Discharge Summaries are documents written by medical professionals that detail a patient's visit to a care facility. They contain a wealth of information crucial for patient care, and automating their generation could significantly reduce the effort required from healthcare professionals, minimize errors, and ensure that critical patient information is easily accessible and actionable. In this work, we explore the use of five Large Language Models on this task, from open-source models (Mistral, Llama 2) to proprietary systems (GPT-3, GPT-4, Gemini 1.5 Pro), leveraging MIMIC-III summaries and notes. We evaluate them using exact-match, soft-overlap, and reference-free metrics. Our results show that proprietary models, particularly Gemini with one-shot prompting, outperformed others, producing summaries with the highest similarity to the gold-standard ones. Open-source models, while promising, especially Mistral after fine-tuning, lagged in performance, often struggling with hallucinations and repeated information. Human evaluation by a clinical expert confirmed the practical utility of the summaries generated by proprietary models. Despite the challenges, such as hallucinations and missing information, the findings suggest that LLMs, especially proprietary models, are promising candidates for automatic discharge summary generation as long as data privacy is ensured.
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