Artificial General Intelligence for Medical Imaging Analysis.

Journal: IEEE reviews in biomedical engineering
Published Date:

Abstract

Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented success in a variety of general domain tasks. Yet, when applied directly to specialized domains like medical imaging, which require in-depth expertise, these models face notable challenges arising from the medical field's inherent complexities and unique characteristics. In this review, we delve into the potential applications of AGI models in medical imaging and healthcare, with a primary focus on LLMs, Large Vision Models, and Large Multimodal Models. We provide a thorough overview of the key features and enabling techniques of LLMs and AGI, and further examine the roadmaps guiding the evolution and implementation of AGI models in the medical sector, summarizing their present applications, potentialities, and associated challenges. In addition, we highlight potential future research directions, offering a holistic view on upcoming ventures. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.

Authors

  • Xiang Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Lin Zhao
    c Key Laboratory of Birth Defects and Related Diseases of Women and Children (Ministry of Education) , West China Second University Hospital Sichuan University , Chengdu , China.
  • Lu Zhang
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Zihao Wu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Zhengliang Liu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Hanqi Jiang
  • Chao Cao
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Shaochen Xu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Yiwei Li
    New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
  • Haixing Dai
    School of Computing, University of Georgia, Athens, GA, United States.
  • Yixuan Yuan
    Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong.
  • Jun Liu
    Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
  • Gang Li
    The Centre for Cyber Resilience and Trust, Deakin University, Australia.
  • Dajiang Zhu
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States.
  • Pingkun Yan
    Philips Research North America, Briarcliff Manor, NY 10510, USA.
  • Quanzheng Li
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.
  • Tianming Liu
    School of Computing, University of Georgia, Athens, GA, United States.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.