人工智能设计和实施的障碍和促进因素,用于临床实践中的乳腺成像诊断:范围审查
Belinda Lokaj1,2,3, Marie-Thérèse Pugliese4, Karen Kinkel5
1Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland. Belinda.Lokaj@hesge.ch.
European radiology
|September 2, 2023
概括
人工智能 (AI) 在乳腺癌诊断方面表现有前途,但实施面临障碍. 本综述确定了AI在乳腺成像中的关键挑战和促进因素,以指导未来的开发和临床整合.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 人工智能 (AI) 为改善乳腺癌诊断提供了巨大的潜力.
- 人工智能在乳腺成像中的临床实施面临着许多实际障碍.
- 了解这些障碍对于成功的AI集成至关重要.
研究的目的:
- 系统地识别和分类人工智能在乳腺癌成像中的实施障碍和促进因素.
- 在临床实践中为开发和部署有效的人工智能解决方案提供信息.
- 引导利益相关者克服改善乳腺癌检测的挑战.
主要方法:
- 在六个主要数据库中对2012年至2022年的文献进行了全面的范围审查.
- 包括的研究描述了乳腺临床成像AI的概念或实施中的障碍和/或促进者.
- 被排除的研究仅关注性能指标或缺乏来自真实患者的临床放射学数据.
主要成果:
- 确定了六个主要障碍:与数据相关的问题,算法透明度 (黑子) 和信任,算法设计和构想,评估和验证过程,法律/道德/经济问题以及教育差距.
- 确定了五个关键促进因素:数据的可用性和质量,可证明的临床影响,强大的算法设计,严格的评估和验证以及全面的教育和培训.
- 这些因素涵盖了从概念到临床部署的整个AI生命周期.
结论:
- 在乳腺癌诊断中成功整合AI需要仔细设计,部署和评估.
- 解决已识别的障碍和利用促进者对于优化人工智能解决方案至关重要.
- 所有利益相关者之间的协作努力是必要的,以推进AI在临床乳腺成像和改善患者的结果.
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