大型语言模型在瘤学:一个审查
David Chen1,2, Rod Parsa1,3, Karl Swanson4
1Radiation Medicine Program, Princess Margaret Hospital Cancer Centre, Toronto, Ontario, Canada.
BMJ oncology
|June 16, 2025
概括
大型语言模型 (LLM) 在瘤学中显示出对临床决策支持和患者护理的希望. 解决像幻觉这样的局限性是整合这些人工智能工具安全有效的关键.
科学领域:
- 人工智能在医学中的应用
- 瘤学 信息学 信息学
背景情况:
- 大型语言模型 (LLM) 展示了先进的自然语言处理能力.
- 它们的整合到医疗保健,特别是瘤学中,正在获得数据合成和决策支持的吸引力.
研究的目的:
- 审查目前医学LLM的现状.
- 为临床医生,患者和研究人员探索瘤学LLM应用.
- 确定未来的研究方向和挑战.
主要方法:
- 本研究是对瘤学LLM现有文献的叙述性综述.
- 它综合了有关当前应用和潜在未来发展的信息.
主要成果:
- 面向临床医生的LLM可以帮助决策和自动化数据提取.
- 面向患者的LLM可以改善信息传播和心理社会支持.
- 关键的限制包括幻觉,概括问题和道德问题.
结论:
- LLM 具有显著的潜力,可以增强瘤学家的专业知识,并提高患者的护理.
- 将LLM集成到复合AI系统中可以提高效率和采用.
- 积极的临床医生参与对于开发和完善瘤学LLM技术至关重要.
关键词:
医学瘤学 医学瘤学相关概念视频
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