临床放射学中的放射学:进步,挑战和未来方向.
K Linton-Reid1, M Chen2, M B Martell3
1Department of Surgery and Cancer, Imperial College London, Du Cane Road, London, W12 0NN, UK; African Institute for Mathematical Sciences, Research and Innovation Centre, Kigali, Rwanda.
Clinical radiology
|December 8, 2025
概括
放射学,提取定量图像特征,正在与人工智能 (AI),包括大语言模型 (LLM) 一起推进. 这些由人工智能驱动的放射性解决方案承诺提高精准医学的诊断和预后能力.
科学领域:
- 医学成像分析 医学成像分析
- 放射学中的人工智能
- 定量成像生物标志物生物标志物
背景情况:
- 放射学从医学图像中提取定量特征,为改善临床放射学提供了潜力.
- 人工智能 (AI) 的整合,特别是大语言模型 (LLM) 和代理AI,正在改变放射性分析.
研究的目的:
- 审查放射学与人工智能的融合,重点关注LLM和代理AI.
- 总结发展情况,突出挑战,并建议放射学采用的未来方向.
- 评估人工智能对放射性方法,标准化和临床整合的影响.
主要方法:
- 关于放射学研究的广泛文献综述.
- 专注于验证框架,标准化,深度学习,法学士和多中心研究.
- 关于诊断准确性,预后性能和可重现性的关键出版物的比较.
主要成果:
- 人工智能,特别是LLM和代理人工智能,为增强放射性分析和临床适用性提供了重大机会.
- 标准化和验证方面的进展正在解决可重现性挑战.
- 放射学显示出提高诊断准确性,预后性能和工作流集成的潜力.
结论:
- 通过人工智能增强的放射学即将成为精确成像的关键.
- 克服可复制性,可解释性和临床整合方面的挑战对于广泛采用至关重要.
- 放射科医生和科学家应该为支持临床决策的AI驱动的放射性工具做好准备.
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