前列腺MRI中的人工智能:当前的证据和临床翻译挑战 - - 叙述性审查
Vlad-Octavian Bolocan1, Alexandru Mitoi1, Oana Nicu-Canareica1,2,3
1Doctoral Program Studies, University of Medicine and Pharmacy "Carol Davila", 050474 Bucharest, Romania.
Journal of imaging
|October 28, 2025
概括
前列腺MRI中的人工智能 (AI) 显示出高研究准确性,但面临着显著的现实世界性能下降和实施障碍. 弥合这一差距需要专注于患者的治疗结果和临床整合的透明标准.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 前列腺癌的诊断 前列腺癌的诊断
背景情况:
- 前列腺MRI中的AI在研究环境中表现出强大的技术性能.
- 人工智能用于前列腺MRI的临床采用落后于其技术进步.
研究的目的:
- 审查前列腺MRI中的AI应用,重点关注现实世界的性能和实施挑战.
- 识别阻碍人工智能在前列腺癌诊断中的临床整合障碍.
主要方法:
- 从2018年1月到2024年12月的文学综合叙事综述.
- 分析了200多项关于前列腺MRI人工智能的研究,包括研究和现实世界的实施数据.
主要成果:
- 人工智能系统在研究中达到高灵敏度 (87%) 和特异性 (72%),但外部验证显示性能下降 (平均12%).
- 报告准则的遵守率低 (31%),代码共享率有限 (11%),模型可用性低 (4%).
- 现实世界的实施面临着整合时间 (3-14个月) 和诸如高假阳性率等问题,导致部署的终止.
结论:
- 在前列腺MRI中,人工智能开发和临床智能之间存在很大的差距.
- 成功的AI整合需要优先考虑患者的结果,透明的报告,可行的经济模式和监管框架.
- 结合方法严谨性,临床相关性和实施科学对于人工智能在前列腺癌治疗中的潜力至关重要.
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