用大模型自动写病历，帮医生省下两小时行政时间
Efficient Fine-Tuning of Large Language Models for Automated Medical Documentation
- 微调LLaMA3-8B模型，从医患对话自动生成病历
- ROUGE达58%，BERTScore-F1达72%，生成准确率高
- 适合医疗AI、临床辅助系统研发者参考
研究表明，医生每花一小时直接看诊，需额外花费近两小时处理电子病历等行政事务。这种过重负担不仅压缩了患者照护时间，还导致医生倦怠和医疗效率下降。为此，本文提出MediGen，一个针对医疗对话自动生成功能的微调大语言模型。通过采用先进开源预训练模型（如LLaMA3-8B）的微调方法，MediGen实现了对临床交流内容的高精度转录与摘要生成。微调后的LLaMA3-8B模型在测试中达到58%的ROUGE分数和72%的BERTScore-F1，表明其生成的病历具有高度准确性与临床相关性。结果表明，MediGen有望显著减轻医生的行政负担，提升医疗效率与医生福祉。
原文摘要 · Abstract (English)
Scientific research indicates that for every hour spent in direct patient care, physicians spend nearly two additional hours on administrative tasks, particularly on electronic health records (EHRs) and desk work. This excessive administrative burden not only reduces the time available for patient care but also contributes to physician burnout and inefficiencies in healthcare delivery. To address these challenges, this study introduces MediGen, a fine-tuned large language model (LLM) designed to automate the generation of medical reports from medical dialogues. By leveraging state-of-the-art methodologies for fine-tuning open-source pretrained models, including LLaMA3-8B, MediGen achieves high accuracy in transcribing and summarizing clinical interactions. The fine-tuned LLaMA3-8B model demonstrated promising results, achieving a ROUGE score of 58% and a BERTScore-F1 of 72%, indicating its effectiveness in generating accurate and clinically relevant medical reports. These findings suggest that MediGen has the potential to significantly reduce the administrative workload on physicians, improving both healthcare efficiency and physician well-being.
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