Prompt Engineering in Clinical Practice: A Comprehensive Guide for Clinicians (Preprint)

Authors:
Preprint
Publisher website
Google Scholar
Abstract
UNSTRUCTURED Large language models (LLMs) present a promising avenue for improving healthcare by enhancing clinical decision making. However, their effectiveness heavily relies on the accurate prompt engineering. This review focuses on understanding and optimizing prompt engineering techniques to guide LLMs in producing clinically relevant and accurate responses. The aim is to provide clinicians with the tools to fully utilize LLMs in practice, ensuring patient-centered care and addressing ethical and operational challenges. Key principles of prompt engineering, such as specificity, contextual relevance and iterative refinement, are essential for the effective use of LLMs. Techniques such as zero-shot, few-shot and chain-of-thought prompting are analyzed in detail to provide clinicians with practical insights into how these approaches influence LLM outcomes. The review also introduces a classification system for prompts - manual versus automatic and discrete versus continuous - to help clinicians apply these models more effectively in different clinical scenarios. Despite advances, challenges remain in ensuring data privacy, maintaining clinical accuracy and handling multimodal data. Effective prompt engineering can significantly improve the performance of LLMs in clinical practice by optimizing input design to provide more accurate, contextually relevant and patient-specific outputs. These improvements enable more efficient clinical decision making. Clinicians need to consider privacy, ensure clinical accuracy and integrate adaptive, contextual and personalized prompts into real-time workflows. By refining prompt engineering practices, clinicians can take full advantage of LLM capabilities, ultimately improving patient outcomes and supporting ethical integration into healthcare.

This publication has 55 references indexed in Scilit: