Customized medical prompts enable Large Language Models (LLM) to effectively address medical dialogue summarization. The process of medical reporting is often time-consuming for healthcare professionals. Implementing medical dialogue summarization techniques presents a viable solution to alleviate this time constraint by generating automated medical reports. The effectiveness of LLMs in this process is significantly influenced by the formulation of the prompt, which plays a crucial role in determining the quality and relevance of the generated reports. In this research, we used a combination of two distinct prompting strategies, known as shot prompting and pattern prompting to enhance the performance of automated medical reporting. The evaluation of the automated medical reports is carried out using the ROUGE score and a human evaluation with the help of an expert panel. The two-shot prompting approach in combination with scope and domain context outperforms other methods and achieves the highest score when compared to the human reference set by a general practitioner. However, the automated reports are approximately twice as long as the human references, due to the addition of both redundant and relevant statements that are added to the report.

翻译：定制化医疗提示能够使大型语言模型有效处理医疗对话摘要任务。对医疗专业人员而言，病历报告的编制过程往往耗时较长。通过生成自动医疗报告，医疗对话摘要技术的实施为缓解这一时间限制提供了可行方案。在此过程中，提示的编写方式显著影响着大型语言模型的表现，对生成报告的质量与相关性起着关键作用。本研究采用两种不同提示策略的组合——即样本提示与模式提示——来提升自动医疗报告的性能。自动医疗报告的评估采用ROUGE评分法，并借助专家小组进行人工评估。结合范围与领域背景的双样本提示方法相较于其他方法表现更优，且在基于全科医生设定的参考标准比较中取得了最高得分。然而，由于报告中添加了冗余和相关的陈述，自动生成的报告长度约为人工参考报告的两倍。