Artificial General Intelligence for Medical Imaging Analysis

doi:10.1109/RBME.2024.3493775

	Artificial General Intelligence for Medical Imaging Analysis
	Xiang Li 1; Lin Zhao 2; Lu Zhang 3; Zihao Wu 2; Zhengliang Liu 2; Hanqi Jiang 4,5; Chao Cao 3; Shaochen Xu 2; Yiwei Li 2; Haixing Dai 2; Yixuan Yuan 6; Jun Liu 7; Gang Li 8; Dajiang Zhu 3; Pingkun Yan 9; Quanzheng Li 1; Wei Liu 10; Tianming Liu 2; Dinggang Shen11,12,13
	2025
发表期刊	IEEE REVIEWS IN BIOMEDICAL ENGINEERING (IF:17.2[JCR-2023])
ISSN	1941-1189
EISSN	1941-1189
卷号	18 页码:113-129
发表状态	已发表
DOI	10.1109/RBME.2024.3493775
摘要	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.
关键词	Artificial general intelligences Foundation models Imaging analysis Intelligence models Language model Large language model Large vision model Large-scales Medical fields Vision model
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20244717412543
原始文献类型	Article in Press
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/445533
专题	生物医学工程学院生物医学工程学院_PI研究组_沈定刚组
作者单位	1.Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA 2.School of Computing, The University of Georgia, Athens, USA 3.Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, USA 4.School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China 5.College of Engineering, The University of Georgia, Athens, USA 6.Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong 7.Department of Radiology, Second Xiangya Hospital, Changsha, China 8.Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, USA 9.Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA 10.Department of Radiation Oncology, Mayo Clinic, Scottsdale, USA 11.School of Biomedical Engineering, ShanghaiTech University, Shanghai, China 12.Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China 13.Shanghai Clinical Research and Trial Center, Shanghai, China
推荐引用方式 GB/T 7714	Xiang Li,Lin Zhao,Lu Zhang,et al. Artificial General Intelligence for Medical Imaging Analysis[J]. IEEE REVIEWS IN BIOMEDICAL ENGINEERING,2025,18:113-129.
APA	Xiang Li.,Lin Zhao.,Lu Zhang.,Zihao Wu.,Zhengliang Liu.,...&Dinggang Shen.(2025).Artificial General Intelligence for Medical Imaging Analysis.IEEE REVIEWS IN BIOMEDICAL ENGINEERING,18,113-129.
MLA	Xiang Li,et al."Artificial General Intelligence for Medical Imaging Analysis".IEEE REVIEWS IN BIOMEDICAL ENGINEERING 18(2025):113-129.