Results of the summarizing process for the arXiv paper: 2304.14670v1

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.

• 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.

Prompt Engineering for Healthcare: Methodologies and Applications

The healthcare industry is rapidly evolving, with new technologies being developed to improve the quality of care. Natural language processing (NLP) has become a key tool in this process, allowing medical professionals to quickly analyze large amounts of data and make informed decisions. Prompt engineering is an important part of this process, providing a way to efficiently generate questions and answers from text-based sources such as electronic health records (EHRs). This review article provides an overview of the development of prompt engineering and its applications in healthcare NLP.

Background

Prompt engineering is a type of natural language processing that uses machine learning algorithms to generate questions from text-based sources. It was first introduced by Jiaqi Wang et al., who proposed a method for automatically generating question-answering systems using prompt engineering techniques. Since then, it has been used in various applications such as text summarization, machine translation, and information retrieval.

Recent Advances

In recent years, there have been significant advances in prompt engineering for healthcare NLP applications. For example, Enze Shi et al., developed an end-to-end system that can accurately answer medical queries based on EHRs using prompt engineering techniques. Additionally, Sigang Yu et al., proposed a novel approach for automatic generation of clinical summaries using deep learning models trained on patient records with prompts generated by prompt engineering methods. Furthermore, Zihao Wu et al., presented a method for automatically translating medical documents into multiple languages using prompt engineering techniques combined with neural machine translation models.

Conclusion

Overall, this review provides an extensive overview of the development and application of prompt engineering in healthcare NLP research and serves as a valuable resource for researchers interested in exploring its potential applications in the medical domain. With the continuous improvement of general large language models such as BERT or GPT-2 ,prompting technology is becoming increasingly important in the healthcare domain due to its ability to effectively generate questions from text sources which can be used to create powerful question answering systems or other types of natural language processing tasks like summarization or machine translation . The authors hope that this review can provide new ideas and inspire ample possibilities for research and application in medical NLP