Artificial General Intelligence for Medical Imaging Analysis
Xiang Li* Lin Zhao* Lu Zhang* Zihao Wu Zhengliang Liu Hanqi Jiang Chao Cao Shaochen Xu Yiwei Li Haixing Dai Yixuan Yuan Jun Liu Gang Li Dajiang Zhu Pingkun Yan Quanzheng Li Wei Liu Tianming Liu Dinggang Shen Thanks: *These authors contributed equally to this work. Thanks: (Corresponding authors: Xiang Li, Tianming Liu, Dinggang Shen) Thanks: Xiang Li and Quanzheng Li are with the Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston 02115, USA. (e-mail: Thanks: Lu Zhang, Chao Cao, and Dajiang Zhu are with the Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington 76019, USA. (e-mail: and Thanks: Lin Zhao, Zihao Wu, Zhengliang Liu, Shaochen Xu, Yiwei Li, Haixing Dai and Tianming Liu are with the School of Computing, The University of Georgia, Athens 30602, USA. (e-mail: {zihao.wu1,zl18864,lin.zhao, hd54134, Thanks: Hanqi Jiang is with the School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China; College of Engineering, The University of Georgia, Athens 30602, USA. (e-mail: Thanks: Yixuan Yuan is with the Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong. (e-mail: Thanks: Jun Liu is with the Department of Radiology, Second Xiangya Hospital, Changsha 410011, China. (e-mail: Thanks: Gang Li is with the Department of Radiology at the University of North Carolina at Chapel Hill, Chapel Hill 27599, USA. (e-mail: Thanks: Pingkun Yan is with the Department of Biomedical Engineering at Rensselaer Polytechnic Institute, Troy, New York 12180, USA. (e-mail: Thanks: Wei Liu is with the Department of Radiation Oncology, Mayo Clinic, Scottsdale 85259, USA. (e-mail: Thanks: Dinggang Shen is with the School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200230, China; Shanghai Clinical Research and Trial Center, Shanghai, 201210, China. (e-mail:
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.
原文 arXiv:2306.05480;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2306.05480v4