解码放射学的大型语言模型:微调和提示工程的策略。

Radiology advances Pub Date : 2025-07-28 eCollection Date: 2025-07-01 DOI:10.1093/radadv/umaf024
Sanaz Vahdati, Elham Mahmoudi, Ali Ganjizadeh, Chiehju Chao, Bradley J Erickson
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引用次数: 0

摘要

大型语言模型(llm)的进步已经证明了在放射学工作流程中自动化复杂任务的复杂潜力。从放射学报告生成和报告总结到研究试验的数据收集,这些模型已被证明是强大的工具。然而,这些模型的最佳实现需要仔细适应专门的医学领域。此外,这些模型往往会产生不真实或事实的信息,这可能会对患者护理和临床决策产生不利影响。诸如微调和提示优化等策略已被证明对消除这些错误很有影响。尽管这些模型经历了快速的更新和改进,但理解快速工程和微调的原则为评估和维护任何LLM部署的性能提供了基础。本文旨在回顾放射学的最新进展,利用微调和及时优化来利用llm的能力。它深入研究了每种策略中的各种技术,它们的优点和局限性,并提出了一个框架,以促进法学硕士与放射学环境的实际整合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Decoding large language models for radiology: strategies for fine-tuning and prompt engineering.

Decoding large language models for radiology: strategies for fine-tuning and prompt engineering.

Decoding large language models for radiology: strategies for fine-tuning and prompt engineering.

The advances in large language models (LLMs) have demonstrated sophisticated potential for automating complex tasks within the radiology workflow. From radiology report generation and report summarization to data collection for research trials, these models have proven to be powerful tools. However, optimal implementation of these models requires careful adaptation to the specialized medical domain. In addition, these models tend to generate information that is not truthful or factual, which can adversely affect patient care and clinical decisions. Strategies such as fine-tuning and prompt optimization have been shown to be impactful in eliminating these errors. Although these models undergo rapid updates and improvements, understanding the principles of prompt engineering and fine-tuning provides a foundation for evaluating and maintaining the performance of any LLM deployment. The current article aims to review the recent advancements in radiology using fine-tuning and prompt optimization to leverage LLMs' capabilities. It delves into various techniques within each strategy, their advantages and limitations, and presents a framework to facilitate the practical integration of LLMs into radiology settings.

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