Scholarlink

<p>Foundation artificial intelligence (AI) models are capable of complex tasks that involve text, medical images, and many other types of data, but have not yet been customized for procedural medicine. This report reviews prior work in deep learning related to interventional radiology (IR), identifying barriers to generalization and&#xa0;deployment at scale. Moreover, this report outlines the potential design of an “IR-GPT” foundation model to provide a unified platform for AI in IR, including data collection, annotation, and training methods—while also contextualizing&#xa0;challenges&#xa0;and highlighting potential downstream applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

IR-GPT: AI Foundation Models to Optimize Interventional Radiology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月26日

查看全文
  • Jacqueline L. Brenner,
  • James T. Anibal,
  • Lindsey A. Hazen,
  • Miranda J. Song,
  • Hannah B. Huth,
  • Daguang Xu,
  • Sheng Xu,
  • Bradford J. Wood

摘要

Foundation artificial intelligence (AI) models are capable of complex tasks that involve text, medical images, and many other types of data, but have not yet been customized for procedural medicine. This report reviews prior work in deep learning related to interventional radiology (IR), identifying barriers to generalization and deployment at scale. Moreover, this report outlines the potential design of an “IR-GPT” foundation model to provide a unified platform for AI in IR, including data collection, annotation, and training methods—while also contextualizing challenges and highlighting potential downstream applications.