Enhancing Medical Task Performance in GPT-4V: A Comprehensive Study on Prompt Engineering Strategies

TL;DR

This study systematically improves GPT-4V's medical imaging performance by developing and testing prompt engineering techniques, significantly enhancing its diagnostic accuracy and clinical relevance in healthcare applications.

Contribution

It introduces 10 effective prompt engineering strategies that substantially boost GPT-4V's interpretative capabilities in medical imaging tasks.

Findings

Enhanced interpretative accuracy in medical imaging

Identification of effective prompt engineering techniques

Improved clinical relevance of GPT-4V outputs

Abstract

OpenAI's latest large vision-language model (LVLM), GPT-4V(ision), has piqued considerable interest for its potential in medical applications. Despite its promise, recent studies and internal reviews highlight its underperformance in specialized medical tasks. This paper explores the boundary of GPT-4V's capabilities in medicine, particularly in processing complex imaging data from endoscopies, CT scans, and MRIs etc. Leveraging open-source datasets, we assessed its foundational competencies, identifying substantial areas for enhancement. Our research emphasizes prompt engineering, an often-underutilized strategy for improving AI responsiveness. Through iterative testing, we refined the model's prompts, significantly improving its interpretative accuracy and relevance in medical imaging. From our comprehensive evaluations, we distilled 10 effective prompt engineering techniques, each fortifying GPT-4V's medical acumen. These methodical enhancements facilitate more reliable, precise, and clinically valuable insights from GPT-4V, advancing its operability in critical healthcare environments. Our findings are pivotal for those employing AI in medicine, providing clear, actionable guidance on harnessing GPT-4V's full diagnostic potential.