从像素到患者:癌症诊断中的深度学习的演变和未来
Yichen Yang1, Hongru Shen1, Kexin Chen2
1Tianjin Cancer Institute, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
Trends in molecular medicine
|December 12, 2024
概括
深度学习的进步通过整合多式联络数据来增强癌症诊断. 特定领域的人工智能 (AI) 模型对于精确的临床决策和改善患者治疗结果变得至关重要.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 生物医学成像技术 生物医学成像技术
背景情况:
- 深度学习 (DL) 已经将癌症诊断从基于像素的分析转变为以患者为中心的方法.
- 最近的神经网络架构显示了生物医学研究的重大演变.
研究的目的:
- 探索用于癌症诊断的神经网络架构的进展.
- 突出人工智能对医学成像解释和多式联络数据集成的影响.
- 倡导特定领域的人工智能和多式联通大型语言模型 (LLM).
主要方法:
- 审查神经网络架构的最新进展.
- 讨论多式联运数据源的整合.
- 重点是开发专门用于临床任务的AI系统.
主要成果:
- 人工智能正在将癌症诊断转向更全面,以患者为中心的护理.
- 多模式的LLM可以整合各种数据,提高诊断精度和效率.
- 人工智能正在从补充工具演变为临床决策的核心组成部分.
结论:
- 特定领域的人工智能系统对于瘤学的复杂临床任务至关重要.
- 多模式LLM具有改善癌症诊断的巨大潜力.
- 整合人工智能对于推进癌症护理和改善患者治疗结果至关重要.
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