  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.
