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<p>Recent advances in AI, especially vision-language foundation models (VLFMs), show promise in automating radiology report generation from complex 3D medical imaging data. Our review analyzes 23 studies on VLFMs, focusing on model architectures, capabilities, training datasets, and evaluation metrics. We discuss AI’s evolution in radiology, emphasizing the need for diverse datasets and standardized metrics, as challenges remain in producing consistent, high-quality reports.</p>

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Vision-language foundation model for 3D medical imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月6日

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  • Jing Wu,
  • Yuli Wang,
  • Zhusi Zhong,
  • Weihua Liao,
  • Natalia Trayanova,
  • Zhicheng Jiao,
  • Harrison X. Bai

摘要

Recent advances in AI, especially vision-language foundation models (VLFMs), show promise in automating radiology report generation from complex 3D medical imaging data. Our review analyzes 23 studies on VLFMs, focusing on model architectures, capabilities, training datasets, and evaluation metrics. We discuss AI’s evolution in radiology, emphasizing the need for diverse datasets and standardized metrics, as challenges remain in producing consistent, high-quality reports.