Prompt Engineering for Healthcare: Methodologies and Applications

TL;DR

This paper reviews recent advances in prompt engineering for healthcare NLP, highlighting its importance in improving medical question-answering, summarization, and translation tasks with large language models.

Contribution

It provides a comprehensive overview of prompt engineering methodologies and applications specifically tailored for medical natural language processing.

Findings

Prompt engineering enhances healthcare NLP task performance.

It is crucial for medical question-answering, summarization, and translation.

The review offers resources and insights for future research in medical NLP.

Abstract

Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks. With the recent advancements in large language models, prompt engineering has shown significant superiority across various domains and has become increasingly important in the healthcare domain. However, there is a lack of comprehensive reviews specifically focusing on prompt engineering in the medical field. This review will introduce the latest advances in prompt engineering in the field of natural language processing for the medical field. First, we will provide the development of prompt engineering and emphasize its significant contributions to healthcare natural language processing applications such as question-answering systems, text summarization, and machine translation. With the continuous improvement of general large language models, the importance of prompt engineering in the healthcare domain is becoming increasingly prominent. The aim of this article is to provide useful resources and bridges for healthcare natural language processing researchers to better explore the application of prompt engineering in this field. We hope that this review can provide new ideas and inspire for research and application in medical natural language processing.