医学放射学报告生成:对当前深度学习方法,趋势和未来方向的系统审查
Amaan Izhar1, Norisma Idris1, Nurul Japar1
1Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.
Artificial intelligence in medicine
|July 23, 2025
概括
本系统性审查分析了用于医学放射学报告生成 (MRRG) 的深度学习. 它涵盖了数据集,模型和评估方法,以在放射学中推进人工智能,提高效率和患者护理.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 手动生成医学放射学报告是耗时的,并使临床工作流程变得紧张.
- 使用深度学习 (DL) 自动化医学放射学报告生成 (MRRG) 可以提高诊断效率并减少患者等待时间.
研究的目的:
- 对基于深度学习的MRRG系统进行全面的系统审查.
- 在基于DL的MRRG中分析当前的数据集,建模方法和评估实践.
- 确定资源和未来方向,以推进放射学领域可靠的人工智能.
主要方法:
- 在主要的学术数据库中按照PRISMA指南进行系统的文献搜索.
- 在323篇获取的文章中,包括和批判性分析了78项相关研究.
- 模型架构,损失函数,数据集,评估指标和优化器的分类和分析.
主要成果:
- 识别了22个广泛使用的数据集,14个评估指标,大约20个损失函数,超过25个视觉骨干和30多个文本骨干.
- 编译链接到现代模型,工具包和预训练资源,以帮助可重复性.
- 对基于DL的MRRG系统的组件进行详细的技术洞察.
结论:
- 深度学习显示了自动化医学放射学报告生成的重大前景.
- 数据集和评估指标的标准化是强有力的比较和进步所需的.
- 进一步的研究和合作对于开发可靠的放射学AI系统至关重要.
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