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.

翻译：OpenAI最新的大型视觉-语言模型（LVLM）GPT-4V(ision)因其在医学应用中的潜力引起了广泛关注。尽管前景可期，近期研究与内部评估表明，其在专业医疗任务中表现欠佳。本文探索了GPT-4V在医学领域的性能边界，特别是在处理内窥镜、CT扫描和MRI等复杂影像数据时的能力。通过利用开源数据集，我们评估了其基础能力，并识别出需要显著改进的领域。本研究聚焦于提示工程——这一常被忽视的优化AI响应能力的策略。通过迭代测试，我们优化了模型的提示词，显著提升了其在医学影像解读中的准确性和相关性。基于全面评估，我们提炼出10种有效的提示工程技巧，每项均增强了GPT-4V的医学认知能力。这些系统性的改进方法使GPT-4V能够提供更可靠、精准且具有临床价值的见解，从而提升其在关键医疗环境中的适用性。本研究成果对医学领域的人工智能从业者具有重要价值，为充分挖掘GPT-4V的诊断潜力提供了清晰且可操作的实践指导。