Visual-language foundation models in medical imaging: A systematic review and meta-analysis of diagnostic and analytical applications.

Journal: Computer methods and programs in biomedicine
Published Date: May 21, 2025

Abstract

BACKGROUND AND OBJECTIVE: Visual-language foundation models (VLMs) have garnered attention for their numerous advantages and significant potential in AI-aided diagnosis and treatment, driving widespread applications in medical tasks. This study analyzes and summarizes the value and prospects of VLMs, highlighting their groundbreaking opportunities in healthcare.

Authors

  • Yiyao Sun
    School of Intelligent Medicine, China Medical University, Shenyang, Liaoning 110122, PR China.
  • Xinran Wen
    Department of Medical Imaging Technology, Second Clinical College, China Medical University, Liaoning 110122, PR China.
  • Yan Zhang
    Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China.
  • Lijun Jin
    School of Intelligent Medicine, China Medical University, Shenyang, Liaoning 110122, PR China.
  • Chunna Yang
    School of Intelligent Medicine, China Medical University, Shenyang, Liaoning 110122, PR China.
  • Qianhui Zhang
    School of Intelligent Medicine, China Medical University, Shenyang, Liaoning 110122, PR China.
  • Mingchen Jiang
    School of Intelligent Medicine, China Medical University, Shenyang, Liaoning 110122, PR China.
  • Zhaoyang Xu
    Google Health, Google LLC, Palo Alto, California, United States of America.
  • Wei Guo
    Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Juan Su
    Department of Dermatology, Xiangya Hospital Central South University, Changsha, China.
  • Xiran Jiang

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