超越人类的局限:在放射学研究中大语言模型的承诺和陷
Chloe Reyes1, Evie Nguyen1, Lauren F Alexander2
1Department of Psychology, Arizona State University, Tempe, AZ.
Journal of computer assisted tomography
|April 22, 2025
概括
大型语言模型 (LLM) 为放射学研究提供了重大潜力,帮助从创意产生到手稿撰写的任务. 研究人员应采用快速工程和其他策略,以最大限度地提高收益,并减轻不准确或有偏见的输出等风险.
科学领域:
- 医疗成像中的人工智能
- 放射学研究应用 放射学研究应用
- 在医疗保健中的自然语言处理.
背景情况:
- 大型语言模型 (LLM) 越来越多地被认为具有改变各种研究领域的潜力.
- 将像ChatGPT这样的AI工具集成到科学工作流中,既带来了机遇,也带来了挑战.
- 放射学研究可以从先进的计算工具中受益,用于文献审查,数据分析和手稿准备.
研究的目的:
- 审查大型语言模型 (LLM) 在放射学研究中的多样化应用.
- 识别和讨论与在这个领域使用LLMs相关的挑战和潜在的陷.
- 为优化LLM使用和减轻研究人员相关风险提供战略.
主要方法:
- 系统地审查当前关于LLM在科学研究中的应用文献,重点是放射学.
- 分析像ChatGPT这样的模型提供的功能,用于研究任务,包括创意生成,文献综合,研究设计和写作.
- 探索缓解技术,如提示工程 (例如,思维链提示),检索增强生成和模型微调等.
主要成果:
- 士课程可以显著提高放射学研究的生产力,通过协助产生想法,文献总结,研究设计,数据分析和手稿起草.
- 关键的挑战包括产生不正确或有偏见的信息的可能性,响应不一致以及道德/隐私方面的担忧.
- 快速工程技术被强调为研究人员改善LLM输出质量和相关性的有效策略.
结论:
- 大型语言模型 (LLM) 代表了强大的工具,可以大大增加放射学研究人员的能力.
- 采用LLM需要仔细考虑其局限性,并实施降低风险的策略.
- 有效利用LLM,特别是通过先进的快速工程,可以安全地提高放射学研究的生产力.
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