克服在放射学人工智能开发和实施中的挑战:全面审查超越监督学习的解决方案
Gil-Sun Hong1, Miso Jang2, Sunggu Kyung3
1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Korean journal of radiology
|September 19, 2023
概括
放射学中的人工智能 (AI) 面临着数据限制和工作流集成等挑战. 本综述讨论了成功采用人工智能的多模式学习和联合学习等解决方案.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 在临床放射学应用中表现有前途.
- 韩国放射学学会确定了2022年人工智能开发和实施的关键挑战.
- 障碍包括数据限制,隐私问题,异质性,可解释性和工作流集成.
研究的目的:
- 审查和讨论对放射学人工智能所面临的挑战的潜在解决方案.
- 探索克服AI在临床实践中广泛采用障碍的策略.
主要方法:
- 审查当前文献和专家讨论的AI在放射学.
- 探索先进的人工智能技术,包括多模式学习,自我监督学习和联合学习.
- 讨论增强数据实用性,模型解释性和临床整合的方法.
主要成果:
- 几种先进的AI方法为当前的局限性提供了潜在的解决方案.
- 诸如纵向和多模式数据集培训,密集培训和生成模型等技术可以解决数据问题.
- 可解释的人工智能,因果学习,联合学习和数字双胞胎被建议用于改进模型性能和集成.
结论:
- 克服数据,可解释性和集成方面的挑战对于放射学AI至关重要.
- 先进的AI技术为提高AI工具的临床实用性和采用提供了路线图.
- 持续的研究和开发对于实现AI在医学成像中的全部潜力至关重要.
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