基于医疗图像的人工智能生成的放射性报告的综合证据:一个范围审查协议
Weibo Feng1, Anthony Yazdani1, Alban Bornet1
1Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
BMJ open
|October 3, 2025
概括
本范围审查绘制了人工智能 (AI) 应用在从医学图像生成放射性报告中的地图. 它确定了当前的趋势,局限性,以及超越简单检测到全面报告生成的进展.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 放射学 信息学 信息学
- 临床决策支持系统 临床决策支持系统
背景情况:
- 增加放射性图像体积和放射科医生的工作量需要人工智能解决方案.
- 大型视觉语言模型 (VLMs) 的出现推动了人工智能驱动的放射性报告生成.
- 目前对人工智能在全面报告生成中的范围和局限性的理解尚不清楚.
研究的目的:
- 系统地绘制AI现有的文献,以从医学图像中生成放射性报告.
- 提供对当前AI在这个领域的状态,范围和局限性的概述.
- 解决缺乏全面的范围审查的问题,特别是最近基础模型的进步.
主要方法:
- 根据PRISMA-ScR指南进行范围审查.
- 在PubMed,Scopus和Web of Science中的搜索 (2016年1月至2025年3月).
- 包括医学图像的AI报告生成的英语,同行评审的原始研究;排除不符合标准的研究.
- 由独立审稿人进行两阶段选;使用CASP检查清单提取数据;叙述和描述合成.
主要成果:
- (将在审查完成后确定)
- (将在审查完成后确定)
- (将在审查完成后确定)
结论:
- (将在审查完成后确定)
- (将在审查完成后确定)
- (将在审查完成后确定)
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