指导如何在放射科高效使用大模型，提升影像与临床信息的结合能力。
Best Practices for Large Language Models in Radiology
- 基于临床经验总结大模型在放射科的最佳实践方法
- 提出有效提示词设计与微调策略以提升结果准确性
- 适合放射科医生和医学AI研究者参考
放射科的核心挑战在于将复杂的影像数据与临床信息整合，生成可操作的洞察。语言的精准运用对管理检查请求、结合临床背景描述与解读影像发现、以及简洁记录和传达结果至关重要。大语言模型（LLMs）的出现为处理放射科海量数据提供了新机遇。尽管这些模型主要为通用设计，但其在特定语言任务中表现出色，即使未经专门训练。充分发挥LLMs在放射科的潜力，需要理解其基本原理，并采取策略应对其特性。本文结合放射科实践与机器学习最新成果，梳理了当前已被验证的有效做法，涵盖优化模型特性、提示工程、微调策略，以及实际应用中的局限性，为从业者提供实用指导。
原文摘要 · Abstract (English)
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, and concisely documenting and communicating the outcomes. The emergence of large language models (LLMs) offers an opportunity to improve the management and interpretation of the vast data in radiology. Despite being primarily general-purpose, these advanced computational models demonstrate impressive capabilities in specialized language-related tasks, even without specific training. Unlocking the potential of LLMs for radiology requires basic understanding of their foundations and a strategic approach to navigate their idiosyncrasies. This review, drawing from practical radiology and machine learning expertise and recent literature, provides readers insight into the potential of LLMs in radiology. It examines best practices that have so far stood the test of time in the rapidly evolving landscape of LLMs. This includes practical advice for optimizing LLM characteristics for radiology practices along with limitations, effective prompting, and fine-tuning strategies.
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