影响放射学人工智能接受的因素:放射学工作流程的系统审查
Jamie Verwey1, Bora Zwart2, Maarten IJzerman2
1Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands. verwey@eshpm.eur.nl.
European radiology
|November 21, 2025
概括
人工智能在放射学中的接受是复杂的,需要的不仅仅是临床准确性. 解决人工智能素养,透明度和涉及不同利益相关者的问题对于成功的临床整合至关重要.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 临床工作流的优化 临床工作流优化
背景情况:
- 人工智能 (AI) 在放射学中的临床实施落后于其技术潜力.
- 利益相关者对人工智能的接受是弥合人工智能验证和广泛采用之间的差距的关键决定因素.
- 了解影响人工智能接受的因素对于其成功融入放射学工作流程至关重要.
研究的目的:
- 系统地审查和识别影响放射学工作流程中临床AI工具的接受和使用的因素.
- 在放射学实践的各个阶段对有关AI的利益相关者的看法进行综合证据.
主要方法:
- 在Ovid Medline,Embase和Web of Science Core中进行了系统的文献搜索,使用ASReview进行记录优先级.
- 纳入标准侧重于评估不同利益相关者接受临床AI模型的研究,而不是将AI视为一般概念.
- 提取的数据包括AI的目的,子专业,模式,利益相关者群体和技术准备水平,随后是与放射学工作流程一致的叙事综合.
主要成果:
- 包括37项研究,主要关注用于图像解释的AI,放射科医生和患者是关键利益相关者.
- 确定了22个不同的接受因素,分为6个主题,强调临床准确性是必要的,但不足以接受.
- 挑战包括人工智能素养,监管和道德问题,缺乏透明度,用户指导不足,以及不代表性的培训数据. 人工智能自动化核心放射科医生任务的阻力更高,而支持工具面临的阻力更小.
结论:
- 关于非临床利益相关者代表性和超出图像解释的AI应用,存在显著的证据差距.
- 在放射学中负责任地实施人工智能需要涉及多种利益相关者的具体案例评估.
- 超越人工智能接受,实现有意义的临床整合需要通过广泛的利益相关者参与来解决算法透明度,数据表示,人工智能素养和监管框架.
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