工作流程
医学
生成语法
基础(证据)
人工智能
生成模型
数据科学
医学影像学
梅德林
机器学习
作者
Neda Tavakoli,Zahra Shakeri,Vrushab Gowda,Konrad Samsel,Arash Bedayat,Ahmadreza Ghasemiesfe,Ulas Bagci,Albert Hsiao,Tim Leiner,James Carr,Daniel Kim,Amir Ali Rahsepar
出处
期刊:Radiology
[Radiological Society of North America]
日期:2025-11-01
卷期号:317 (2): e242961-e242961
标识
DOI:10.1148/radiol.242961
摘要
Foundation models can streamline imaging workflows, improve diagnostic accuracy, enable personalized treatment, and, via integrated generative artificial intelligence, create synthetic images to augment scarce real-world data; however, successful adoption requires explainability, trust, and workflow integration.
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