# Prompt Engineering for Healthcare: Methodologies and Applications

**Authors:** Jiaqi Wang, Enze Shi, Sigang Yu, Zihao Wu, Chong Ma, Haixing Dai, Qiushi Yang, Yanqing Kang, Jinru Wu, Huawen Hu, Chenxi Yue, Haiyang Zhang, Yiheng Liu, Xiang Li, Bao Ge, Dajiang Zhu, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang  
**arXiv:** [2304.14670](https://arxiv.org/abs/2304.14670)  
**Date:** 2023-04-28  
**Summary page:** https://www.summarizepaper.com/fr/paper/2304.14670v1/

## Abstract

This review will introduce the latest advances in prompt engineering in the field of natural language processing (NLP) for the medical domain. First, we will provide a brief overview of the development of prompt engineering and emphasize its significant contributions to healthcare NLP 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 NLP researchers to better explore the application of prompt engineering in this field. We hope that this review can provide new ideas and inspire ample possibilities for research and application in medical NLP.