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。

通过基于 Transformer 的提示工程提升自动医疗报告的摘要性能

Enhancing Summarization Performance through Transformer-Based Prompt  Engineering in Automated Medical Reporting

Medical dialogue summarization is challenging due to the unstructured nature
of medical conversations, the use of medical terminology in gold summaries, and
the need to identify key information across multiple symptom sets. We present a
novel system for the Dialogue2Note Medical Summarization tasks in the MEDIQA
2023 Shared Task. Our approach for section-wise summarization (Task A) is a
two-stage process of selecting semantically similar dialogues and using the
top-k similar dialogues as in-context examples for GPT-4. For full-note
summarization (Task B), we use a similar solution with k=1. We achieved 3rd
place in Task A (2nd among all teams), 4th place in Task B Division Wise
Summarization (2nd among all teams), 15th place in Task A Section Header
Classification (9th among all teams), and 8th place among all teams in Task B.
Our results highlight the effectiveness of few-shot prompting for this task,
though we also identify several weaknesses of prompting-based approaches. We
compare GPT-4 performance with several finetuned baselines. We find that GPT-4
summaries are more abstractive and shorter. We make our code publicly
available.

研究围绕通过两阶段方法和使用 GPT-4 模型进行对话摘要，通过比较与基线的性能表现发现 few-shot prompting 的有效性，但是也指出了它的一些局限性。

MEDIQA-Chat 2023 中的 SummQA：使用 GPT-4 进行上下文学习的医学摘要

SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical  Summarization

In medical dialogue summarization, summaries must be coherent and must
capture all the medically relevant information in the dialogue. However,
learning effective models for summarization require large amounts of labeled
data which is especially hard to obtain. We present an algorithm to create
synthetic training data with an explicit focus on capturing medically relevant
information. We utilize GPT-3 as the backbone of our algorithm and scale 210
human labeled examples to yield results comparable to using 6400 human labeled
examples (~30x) leveraging low-shot learning and an ensemble method. In
detailed experiments, we show that this approach produces high quality training
data that can further be combined with human labeled data to get summaries that
are strongly preferable to those produced by models trained on human data alone
both in terms of medical accuracy and coherency.

通过利用 GPT-3 作为算法骨干并采用低样本学习和集成方法，我们介绍了一种算法来创建合成训练数据，以明确关注捕捉医学相关信息，结果相当于采用 6400 个人标记的示例，能够制作出高质量的摘要，就医学准确性和连贯性而言，这种方法产生的摘要明显优于单纯使用人工标记数据进行训练的模型生成的摘要。