在放射学中实施人工智能的错误识别:叙述性审查
Nikolaos Stogiannos1, Renato Cuocolo2, Tugba Akinci D'Antonoli3
1Department of Midwifery & Radiography, C.R.R.A.G. Research Group, School of Health and Medical Sciences, City St George's, University of London, London, UK; Magnitiki Tomografia Kerkiras, Corfu, Greece; European Federation of Radiographer Societies (EFRS), Cumieira, Portugal.
放射学中的人工智能 (AI) 失败可能会阻碍性能和采用. 本次审查确定了人工智能模型,基础设施和人为因素中的问题,以提出更好的人工智能集成解决方案.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 临床实施 临床实施
背景情况:
- 人工智能 (AI) 在临床环境中实施,特别是放射学,面临着许多挑战.
- 失败可能会对人工智能算法性能,临床采用,工作流效率和成本效益产生负面影响.
研究的目的:
- 综合审查和讨论放射学AI失败的各种原因.
- 分析有关人工智能模型,技术基础设施和人工智能实施中的人类因素的公布证据.
主要方法:
- 这篇叙事综述综合了有关放射学AI失败的现有文献.
- 分析侧重于三个核心组成部分:人工智能模型 (生命周期),技术基础设施 (硬件/软件) 和人为因素.
主要成果:
- 人工智能失败源于人工智能模型本身的问题,支持技术基础设施和与人类相关的元素.
- 详细介绍了这些领域的特定故障示例.
结论:
- 了解人工智能故障的根本原因对于优化其在放射学中的使用至关重要.
- 提出的解决方案旨在加强人工智能工具在临床实践中的成功采用和整合.
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