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Prompt Engineering Paradigms for Medical Applications: Scoping Review.

Jamil Zaghir1,2, Marco Naguib3, Mina Bjelogrlic1,2

  • 1Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.

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Summary
This summary is machine-generated.

Prompt engineering for medical applications is emerging, with prompt design being most common. Many studies lack baselines and detailed reporting, hindering future research in clinical natural language processing.

Keywords:
LLMsNLPclinical natural language processingclinical practicecomputer sciencelarge language modelsmedical applicationmedical applicationsmedical informaticsmedical textsmedicinenatural language processingprivacyprompt designprompt engineeringprompt learningprompt tuningscoping review

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Prompt engineering enhances large language model (LLM) capabilities, particularly vital in medicine due to specialized language.
  • Clinical natural language processing (NLP) faces challenges with complex medical texts and privacy.
  • Prompt engineering offers a novel method for extracting clinical information from medical texts.

Purpose of the Study:

  • To review prompt engineering research and technical approaches in medical applications.
  • To provide an overview of opportunities and challenges for clinical practice.
  • To establish reporting guidelines for future medical prompt engineering studies.

Main Methods:

  • Searched medical, computer science, and informatics databases, including preprints.
  • Included studies from 2022-2024 using prompt learning (PL), prompt tuning (PT), and prompt design (PD).
  • Extracted data on prompt paradigms, LLMs, languages, topics, baselines, and strategies.

Main Results:

  • 114 prompt engineering studies were included; prompt design (PD) was most prevalent (78 papers).
  • ChatGPT was the most used LLM; 7 studies used it with sensitive clinical data.
  • Chain-of-thought was the most frequent PD technique; many PD studies lacked non-prompt baselines.

Conclusions:

  • Prompt engineering shows promise in medicine, but reporting inconsistencies exist.
  • Guidelines are provided to improve future research and reporting standards.
  • Summarized data aims to facilitate further advancements in medical prompt engineering.