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放射学研究中对大语言模型的利用:全面用户指南

Leveraging Large Language Models in Radiology Research: A Comprehensive User Guide.

作者信息

Brown Joshua D, Lenchik Leon, Doja Fayhaa, Kaviani Parisa, Judd Dallin, Probyn Linda, Lee Sterling, Goodman Eric M, Sedeh Ashkan Eighaei, Makary Mina S, Lee Ryan K, Retrouvey Michele

机构信息

Department of Radiology and Imaging Sciences, Emory University, 1364 Clifton Rd, Atlanta, GA 30322 (J.D.B.).

Department of Radiology, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157 (L.L.).

出版信息

Acad Radiol. 2025 May;32(5):3082-3091. doi: 10.1016/j.acra.2024.11.053. Epub 2025 Jan 6.

Abstract

Large Language Models (LLMs) such as ChatGPT have been increasingly integrated into radiology research, revolutionizing the research landscape. The Radiology Research Alliance (RRA) of the Association for Academic Radiology (AAR) has convened a Task Force to develop this guide to help radiology researchers responsibly adopt LLM technologies. LLMs can improve various phases of the research process by helping to automate literature reviews, generate research questions, analyze complex datasets, and draft manuscripts. Despite its potential to improve research efficiency, implementation of LLMs poses challenges, especially for users with limited artificial intelligence (AI) experience. This review focuses on approaches to using LLMs in each phase of the research process and addresses prompt engineering to improve interaction with LLMs as well as ethical concerns to ensure scientific integrity. By combining human expertize with AI-driven efficiency, radiology researchers can foster innovation, advance knowledge, and enhance patient care.

摘要

诸如ChatGPT之类的大语言模型(LLMs)已越来越多地融入放射学研究,彻底改变了研究格局。学术放射学会(AAR)的放射学研究联盟(RRA)召集了一个特别工作组来编写本指南,以帮助放射学研究人员负责任地采用大语言模型技术。大语言模型可以通过帮助实现文献综述自动化、生成研究问题、分析复杂数据集以及起草手稿来改进研究过程的各个阶段。尽管大语言模型有提高研究效率的潜力,但其应用也带来了挑战,尤其是对于人工智能(AI)经验有限的用户。本综述重点关注在研究过程的每个阶段使用大语言模型的方法,并探讨提示工程以改善与大语言模型的交互以及伦理问题以确保科学诚信。通过将人类专业知识与人工智能驱动的效率相结合,放射学研究人员可以促进创新、推进知识并改善患者护理。

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