# 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/en/paper/2304.14670v1/

## Key points

- Introduction of prompt engineering in natural language processing (NLP) for the medical domain
- Overview of prompt engineering's contributions to healthcare NLP applications such as question-answering systems, text summarization, and machine translation
- Increasing importance of prompt engineering in the healthcare domain due to improvements in general large language models
- Aim of the article is to provide resources and bridges for healthcare NLP researchers to explore its application in this field
- Hope that the review can inspire new ideas and possibilities for research and application in medical NLP
- Authors include 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 , and Shu Zhang.
- Overall provides an extensive overview of prompt engineering in healthcare NLP and serves as a valuable resource for researchers interested in exploring its applications in the medical domain.

## Summary

This review article titled "Prompt Engineering for Healthcare: Methodologies and Applications" introduces the latest advances in prompt engineering in the field of natural language processing (NLP) for the medical domain. The authors provide a comprehensive overview of the development of prompt engineering and 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, prompt engineering is becoming increasingly important in the healthcare domain. The aim of this article is to provide useful resources and bridges for healthcare NLP researchers to better explore its application in this field. The authors hope that this review can provide new ideas and inspire ample possibilities for research and application in medical NLP. The authors of this article include 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 , and Shu Zhang. Overall,this review provides an extensive overview of prompt engineering in healthcare NLP and serves as a valuable resource for researchers interested in exploring its applications in the medical domain.