A 20-year retrospect and prospect of medical imaging artificial intelligence in China

ABSTRACT

The development of medical imaging, artificial intelligence (AI) and clinical applications derived from AI-based medical imaging has been recognized in past two decades. The improvement and optimization of AI-based technologies have been significantly applied to various of clinical scenarios to strengthen the capability and accuracy of diagnosis and treatment. Nowadays, China has been playing a major role and making increasing contributions in the field of AI-based medical imaging. More worldwide researchers in the context of AI-based medical imaging have contributed to universities and institutions in China. The number of research papers published by Chinese scholars in top international journals and conferences like AI-based medical imaging has dramatically increased annually. Some AI-based medical imaging international conferences and summits have been successfully held in China. There is an increasing number of traditional medical, internet technology and AI enterprises contributing to the research and development of AI-based medical imaging products. More collaborative medical research projects have been implemented for AI-based medical imaging. The Chinese administrations have also planned relevant policies and issued strategic plans for AI-based medical imaging, and included the intelligent medical care as one of the key tasks for the development of new generation of AI in China in 2030. In order to review China's contribution for AI-based medical imaging, we conducted a 20 years review for AI-based medical imaging forecasting in China. Specifically, we summarized all papers published by Chinese scholars in the top AI-based medical imaging journals and conferences including Medical Image Analysis (MedIA), IEEE Transactions on Medical Imaging (TMI), and Medical Image Computing and Computer Assisted Intervention (MICCAI) in the past 20 years. The detailed quantitative metrics like the number of published papers, authorship, affiliations, author's cooperation network, keywords, and the number of citations were critically reviewed. Meanwhile, we briefly summarized some milestone events of AI-based medical imaging in China, including the renowned international and domestic conferences in AI-based medical imaging held in China, the release of the "The White Paper on Medical Imaging Artificial Intelligence in China", as well as China's contributions during the COVID-19(corona virus desease 2019) pandemic. For instance, the total number of published papers in the past 20 years and the proportion of published papers in 2021 by Chinese affiliations have reached to 333 and 37.29% in MedIA, 601 and 42.26% in TMI, and 985 and 44.26% in MICCAI. In those published papers by Chinese institutes, the proportion of the first and the corresponding Chinese authors is 71.97% in MedIA, 69.64% in TMI, and 77.4% in MICCAI in 2021. The average number of citations per paper by Chinese institutes is 22, 28, and 9 in MedIA, TMI, and MICCAI, respectively. In all published papers by Chinese institutes, the predominant research methods were transformed from conventional approaches to sparse representation in 2012, and to deep learning in 2017, which were close to the latest developmental trend of AI technologies. Besides conventional applications such as medical image registration, segmentation, reconstruction and computer-aided diagnosis, etc., the published papers also focused on healthcare quick response in terms of COVID-19 pandemic. The China-derived data and source codes have been sharing in the global context to facilitate worldwide AI-based medical imaging research and education. Our analysis could provide a reference for international scientific research and education for newly Chinese scholars and students based on the growth of the global AI-based medical imaging. Finally, we promoted technology forecasting on AI-based medical imaging as mentioned below. First, strengthen the capability of deep learning for AI-based medical imaging further, including optimal and efficient deep learning, generalizable deep learning, explainable d ep learning, fair deep learning, and responsible and trustworthy deep learning. Next, improve the availability and sharing of high-quality and benchmarked medical imaging datasets in the context of AI-based medical imaging development, validation, and dissemination are harnessed to reveal the key challenges in both basic scientific research and clinical applications. Third, focus on the multi-center and multi-modal medical imaging data acquisition and fusion, as well as integration with natural language such as diagnosis report. Fourth, awake doctors' intervention further to realize the clinical applications of AI-based medical imaging. Finally, conduct talent training, international collaboration, as well as sharing of open source data and codes for worldwide development of AI-based medical imaging. © 2022, Editorial Office of Journal of Image and Graphics. All right reserved.