Prompt engineering for healthcare: Methodologies and applications
Jiaqi Wang, Enze Shi, Sigang Yu, Zihao Wu, Huawen Hu, Chong Ma, Haixing Dai, Qiushi Yang, Yanqing Kang, Jinru Wu, Chenxi Yue, Haiyang Zhang, Yiheng Liu, Yi Pan, Zhengliang Liu, Lichao Sun, Xiang Li, Bao Ge, Xi Jiang, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang
Abstract
Prompt engineering is a critical technique in the field of natural language processing (NLP) 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 (LLM), 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 NLP for the medical field. First, we will provide 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 LLM, 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 for research and application in medical NLP.