With all the fast growth of cyberspace, the improvement of computer abilities, additionally the continuous development of algorithms, deep learning has developed quickly in the past few years and has now been commonly applied in many fields. Previous research indicates that deep discovering has actually a great performance in image handling, and deep learning-based medical picture handling might help resolve the problems experienced by standard health image processing. This technology features attracted the interest of many scholars in the fields of computer technology and medicine. This study primarily summarizes the information construction of deep learning-based health image processing research through bibliometric analysis and explores the research hotspots and feasible development trends in this industry. Retrieve the Web of Science Core Collection database making use of the search phrases “deep learning,” “medical image processing,” and their particular synonyms. Use CiteSpace for visual evaluation of authors, institutions, countries, key words, co-cited referis, segmentation, picture, algorithm, and synthetic cleverness. The research focus and trends tend to be slowly shifting toward more complicated and organized instructions, and deep discovering technology will continue to play a crucial role.The application of deep learning in medical picture handling is starting to become progressively typical, and there are numerous energetic authors, establishments, and nations in this area. Existing analysis in medical image handling mainly centers on deep understanding, convolutional neural networks, classification, analysis, segmentation, picture, algorithm, and synthetic cleverness. The study focus and styles tend to be slowly shifting toward more complex and systematic guidelines, and deep discovering technology continues to play an important role.Human-centered artificial intelligence (HCAI) has VB124 gained momentum when you look at the scientific discourse but still lacks quality. In certain, disciplinary variations concerning the scope of HCAI are becoming evident and had been criticized, phoning for a systematic mapping of conceptualizations-especially with regard to the task framework. This article compares exactly how peoples aspects and ergonomics (HFE), therapy, human-computer conversation (HCI), information technology, and person education view HCAI and discusses their normative, theoretical, and methodological methods toward HCAI, plus the implications for analysis and training. It will be argued that an interdisciplinary strategy is critical for developing, moving, and applying HCAI at the job. Furthermore, it will likely be shown that the displayed disciplines are well-suited for conceptualizing HCAI and taking it into practice merit medical endotek being that they are united in one aspect all of them position the human being in the middle of their concept and research. Numerous critical aspects for successful HCAI, as well as minimum areas of activity, were more identified, such peoples capacity and controllability (HFE viewpoint), autonomy and trust (psychology and HCI viewpoint), discovering and teaching designs across target groups (adult knowledge perspective), whenever information behavior and information literacy (information science point of view). As such, this article lays the floor for a theory of human-centered interdisciplinary AI, i.e., the Synergistic Human-AI Symbiosis concept (SHAST), whose conceptual framework and founding pillars would be introduced.COVID-19 has brought significant modifications to your political, social, and technological landscape. This report explores the introduction and worldwide scatter associated with the disease and centers on the part of Artificial Intelligence (AI) in containing its transmission. To the most readily useful of our understanding, there’s been no systematic presentation of the early pictorial representation for the disease’s spread. Additionally, we outline different domains where AI makes an important impact during the pandemic. Our methodology requires looking relevant articles on COVID-19 and AI in leading databases such as PubMed and Scopus to determine the techniques AI has dealt with pandemic-related challenges and its prospect of further help. While study suggests that AI has not yet completely recognized its potential against COVID-19, likely because of information high quality and variety secondary infection limits, we examine and identify key areas where AI has been crucial in preparing the fight against any sudden outbreak of the pandemic. We additionally propose techniques to maximize the usage of AI’s capabilities in this regard.Adaptive testing has actually a lengthy but mostly unrecognized history. The advent of computer-based evaluation has established new opportunities to include transformative examination into conventional programs of research.
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