在放射学研究中利用大语言模型:全面的用户指南
Joshua D Brown1, Leon Lenchik2, Fayhaa Doja3
1Department of Radiology and Imaging Sciences, Emory University, 1364 Clifton Rd, Atlanta, GA 30322 (J.D.B.).
Academic radiology
|January 7, 2025
概括
本指南有助于放射学研究人员在工作中使用大型语言模型 (LLM). 法律法学可以提高研究效率和创新,同时解决科学诚信的伦理问题.
科学领域:
- 医疗成像中的人工智能
- 放射学研究方法 放射学研究方法
背景情况:
- 大型语言模型 (LLM) 在放射学研究中越来越多地被采用.
- 学术放射学协会的放射学研究联盟成立了一个工作组,指导LLM整合.
- 通过LLM提出了挑战,特别是对于那些对人工智能 (AI) 新的人来说.
研究的目的:
- 为在放射学研究中负责任地采用LLM技术提供指南.
- 概述利用LLMs在所有研究阶段的方法.
- 在科学研究中应对人工智能的及时工程和伦理考虑.
主要方法:
- 在文献评论,研究问题生成,数据分析和手稿起草中审查LLM应用程序.
- 探索用于有效LLM互动的快速工程技术.
- 讨论保持科学完整性的伦理问题.
主要成果:
- 在各种研究任务中,LLM可以显著提高效率.
- 有效的快速工程对于最佳的LLM性能至关重要.
- 解决伦理方面的考虑对于负责任的AI实施至关重要.
结论:
- 通过LLMs将人类专业知识与人工智能驱动的效率相结合,可以促进放射学领域的创新.
- 通过LLM有潜力推进知识和改善患者护理.
- 负责任的实施是最大限度地利用LLMs在放射学研究中的好处的关键.
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