The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal con- tact to patients, providing the best protection to the imaging technicians. Also, AI can improve work efficiency by accurate delineation of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer- aided platforms help radiologists make clinical decisions, Manuscript received April 6, 2020; revised April 11, 2020; accepted April 11, 2020. Date of publication April 16, 2020; date of current version January 22, 2021. This work was supported in part by the Shanghai Science and Technology Foundation under Grants 18010500600 and 19QC1400600, in part by the National Key Research and Development Program of China under Grant 2018YFC0116400, and in part by the Natural Science Foundation of Jiangsu Province under Grant BK20181339. (Feng Shi, Jun Wang, and Jun Shi contributed equally to this work.) (Corresponding author: Dinggang Shen.) Feng Shi and Dinggang Shen are with the Department of Re- search and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China (e-mail: [email protected]; [email protected]). Jun Wang and Jun Shi are with the Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai Institute for Advanced Communication and Data Science, School of Communication and In- formation Engineering, Shanghai University, Shanghai 200444, China (e-mail: [email protected]; [email protected]). Ziyan Wu is with the United Imaging Intelligence, Cambridge, MA 02140 USA (e-mail: [email protected]). Qian Wang is with the Institute for Medical Imaging Technology School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China (e-mail: [email protected]). Zhenyu Tang is with the Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, China (e-mail: [email protected]). Kelei He is with the Medical School of Nanjing University, Nan- jing 210093, China, and also with the National Institute of Health- care Data Science, Nanjing University, Nanjing 210093, China (e-mail: [email protected]). Yinghuan Shi is with the National Key Laboratory for Novel Software and Technology, Nanjing University, Nanjing 210093, China, and also with the National Institute of Healthcare Data Science, Nanjing Univer- sity, Nanjing 210093, China (e-mail: [email protected]). Digital Object Identifier 10.1109/RBME.2020.2987975 i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19.