Summary of Prompt Engineering Paradigms For Medical Applications: Scoping Review and Recommendations For Better Practices, by Jamil Zaghir et al.
Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices
by Jamil Zaghir, Marco Naguib, Mina Bjelogrlic, Aurélie Névéol, Xavier Tannier, Christian Lovis
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive review of recent studies (2022-2024) that employ prompt engineering techniques in the medical domain to harness the potential of large language models (LLMs). Specifically, it focuses on three primary techniques: prompt learning (PL), prompt tuning (PT), and prompt design (PD). The study highlights that PD is the most prevalent approach, with 78 articles using this method. Moreover, Chain-of-Thought emerges as a common technique used in prompt engineering. The paper also notes that while PL and PT studies typically provide baselines for evaluating prompt-based approaches, many PD studies lack non-prompt-related baselines. To guide future research contributions, the study provides tables and figures summarizing existing work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Prompt engineering is crucial to unlocking the potential of large language models (LLMs) in medicine. The paper reviews 114 recent studies that apply prompt engineering techniques in medicine. It finds that prompt design (PD) is the most common approach, used in 78 articles. The study also notes that some papers use terms like prompt learning (PL), prompt tuning (PT), and PD interchangeably. ChatGPT is the most commonly used LLM, with seven papers using it to process sensitive clinical data. The paper provides recommendations for future research contributions. |
Keywords
» Artificial intelligence » Prompt
