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.

通过使用两种不同的提示策略（即 shot 提示和 pattern 提示），该研究通过结合范围和领域上下文，改善了自动医学报告的性能，并且获得了最高分数，超过了一般执业医生的参考文献。然而，由于报告中添加了冗余和相关陈述，自动报告的长度大约是人工参考的两倍。