Prompt Engineering for Healthcare: Methodologies and Applications (2304.14670v2)
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 LLMs, 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 LLMs, 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.
- Jiaqi Wang (218 papers)
- Enze Shi (13 papers)
- Sigang Yu (4 papers)
- Zihao Wu (100 papers)
- Chong Ma (28 papers)
- Haixing Dai (39 papers)
- Qiushi Yang (10 papers)
- Yanqing Kang (2 papers)
- Jinru Wu (2 papers)
- Huawen Hu (6 papers)
- Chenxi Yue (2 papers)
- Haiyang Zhang (56 papers)
- Yiheng Liu (24 papers)
- Xiang Li (1002 papers)
- Bao Ge (17 papers)
- Dajiang Zhu (68 papers)
- Yixuan Yuan (67 papers)
- Dinggang Shen (153 papers)
- Tianming Liu (161 papers)
- Shu Zhang (286 papers)