遵守医学成像人工智能检查清单 (CLAIM):一个总体审查,包括全面的两级分析分析
Burak Koçak1, Fadime Köse1, Ali Keleş1
1University of Health Sciences, Başakşehir Çam and Sakura City Hospital, Department of Radiology, İstanbul, Türkiye.
Diagnostic and interventional radiology (Ankara, Turkey)
|February 12, 2025
概括
这项研究评估了人工智能研究中人工智能医学成像检查清单 (CLAIM) 的遵守情况,发现了重大报告差距和报告不足的项目. 提高CLAIM合规性可以提高医学成像AI研究的透明度和可重复性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 研究方法研究方法研究方法学
背景情况:
- 人工智能 (AI) 在医学成像方面正在迅速发展.
- 标准化报告准则对于人工智能研究的透明度和可重复性至关重要.
- 医学成像人工智能检查清单 (CLAIM) 旨在提高报告质量.
研究的目的:
- 综合评估医疗成像人工智能检查清单 (CLAIM) 在医学成像AI文献中的遵守情况.
- 从以前的系统和非系统审查中汇总数据,以评估CLAIM合规性.
- 确定影响CLAIM坚持和共同批评的因素.
主要方法:
- 我们对PubMed,Scopus和谷歌学者进行了系统的搜索.
- 使用CLAIM评估医学成像AI研究的评论在审查和研究层面都被分析.
- 分析了CLAIM遵守指标,基线特征,影响因素和批评.
主要成果:
- 审查层面的分析显示,中位数的CLAIM得分为26分,合规度为66%.
- 对421项独特研究 (1997-2024) 的研究水平分析显示,中位数的CLAIM得分为26分,合规度为68%.
- 遵守与期刊影响因子,出版年和放射学子领域相关;指南发布后的遵守情况有所改善. 在超过50%的研究中,有11个项目被报告不足.
结论:
- 在医学成像人工智能研究中存在重大报告缺口和报告不足的项目.
- 为研究人员和期刊提供可操作的见解,以提高透明度,可复制性和报告质量.
- 改善CLAIM的遵守对于在医学成像中推进可靠的AI至关重要.
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