Meta-Radiology· 2025Q1· Review
Prompt engineering for healthcare: Methodologies and applications
- 139citations
- Q1SCImago
- 2025year
Short summary
This review synthesizes recent advances in prompt engineering for medical Natural Language Processing (NLP), highlighting its growing importance for Large Language Models (LLMs) in healthcare.
AI-generated from the title and abstract; the full text is not read.
Key points
- Prompt engineering optimizes inputs for language models to improve task performance.
- Its importance in healthcare NLP is growing with advancements in general Large Language Models (LLMs).
- The review covers applications like question-answering, text summarization, and machine translation in medicine.
- It aims to provide resources and inspiration for medical NLP researchers.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Meta-Radiology, 2025 · DOI ↗
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Field: Artificial Intelligence
Artificial IntelligenceComputer Science